mirror of
https://github.com/bytedance/deer-flow.git
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9e6ce2f98e
serialize_lc_object() passed floats through unchanged, so a non-finite
value (NaN/inf/-inf) survived serialization and broke the two display
paths that share it with opposite JSON policies: the SSE wire
(format_sse / Redis bridge use the lenient default and emit bare
NaN/Infinity tokens the langgraph-sdk/useStream decoder rejects,
breaking the live stream) and the REST surface (Starlette JSONResponse
uses allow_nan=False and raises, turning a thread read into HTTP 500).
Non-finite floats reach the serializer from ordinary runtime data:
json.loads("1e400") yields inf for a tool parsing external JSON, a
provider logprob of -inf for a zero-probability token, or a divide-by-
zero ratio. They round-trip inside Python and checkpoint persistence and
only fail at the JSON boundary.
Normalize non-finite floats to None and recurse into model_dump()/dict()
output so nested values (e.g. a message's response_metadata) are
normalized too. This is a display/wire normalization only; persisted
checkpoint state is never routed through this serializer. Finite floats
are unchanged.
Signed-off-by: CorgiBoyG <111257566+CorgiBoyG@users.noreply.github.com>
503 lines
16 KiB
Python
503 lines
16 KiB
Python
"""Tests for deerflow.runtime.serialization."""
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from __future__ import annotations
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class _FakePydanticV2:
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"""Object with model_dump (Pydantic v2)."""
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def model_dump(self):
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return {"key": "v2"}
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class _FakePydanticV1:
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"""Object with dict (Pydantic v1)."""
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def dict(self):
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return {"key": "v1"}
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class _Unprintable:
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"""Object whose str() raises."""
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def __str__(self):
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raise RuntimeError("no str")
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def __repr__(self):
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return "<Unprintable>"
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def test_serialize_none():
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from deerflow.runtime.serialization import serialize_lc_object
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assert serialize_lc_object(None) is None
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def test_serialize_primitives():
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from deerflow.runtime.serialization import serialize_lc_object
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assert serialize_lc_object("hello") == "hello"
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assert serialize_lc_object(42) == 42
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assert serialize_lc_object(3.14) == 3.14
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assert serialize_lc_object(True) is True
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def test_serialize_dict():
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from deerflow.runtime.serialization import serialize_lc_object
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obj = {"a": _FakePydanticV2(), "b": [1, "two"]}
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result = serialize_lc_object(obj)
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assert result == {"a": {"key": "v2"}, "b": [1, "two"]}
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def test_serialize_list():
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from deerflow.runtime.serialization import serialize_lc_object
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result = serialize_lc_object([_FakePydanticV1(), 1])
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assert result == [{"key": "v1"}, 1]
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def test_serialize_tuple():
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from deerflow.runtime.serialization import serialize_lc_object
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result = serialize_lc_object((_FakePydanticV2(),))
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assert result == [{"key": "v2"}]
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def test_serialize_pydantic_v2():
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from deerflow.runtime.serialization import serialize_lc_object
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assert serialize_lc_object(_FakePydanticV2()) == {"key": "v2"}
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def test_serialize_pydantic_v1():
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from deerflow.runtime.serialization import serialize_lc_object
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assert serialize_lc_object(_FakePydanticV1()) == {"key": "v1"}
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def test_serialize_fallback_str():
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from deerflow.runtime.serialization import serialize_lc_object
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result = serialize_lc_object(object())
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assert isinstance(result, str)
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def test_serialize_fallback_repr():
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from deerflow.runtime.serialization import serialize_lc_object
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assert serialize_lc_object(_Unprintable()) == "<Unprintable>"
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def test_serialize_channel_values_strips_pregel_keys():
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from deerflow.runtime.serialization import serialize_channel_values
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raw = {
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"messages": ["hello"],
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"__pregel_tasks": "internal",
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"__pregel_resuming": True,
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"__interrupt__": [{"value": "ask_human", "resumable": True}],
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"title": "Test",
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}
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result = serialize_channel_values(raw)
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assert "messages" in result
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assert "title" in result
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assert "__pregel_tasks" not in result
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assert "__pregel_resuming" not in result
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assert "__interrupt__" in result
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assert isinstance(result["__interrupt__"], list)
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assert len(result["__interrupt__"]) == 1
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assert result["__interrupt__"][0]["value"] == "ask_human"
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def test_serialize_channel_values_serializes_objects():
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from deerflow.runtime.serialization import serialize_channel_values
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result = serialize_channel_values({"obj": _FakePydanticV2()})
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assert result == {"obj": {"key": "v2"}}
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def test_serialize_messages_tuple():
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from deerflow.runtime.serialization import serialize_messages_tuple
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chunk = _FakePydanticV2()
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metadata = {"langgraph_node": "agent"}
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result = serialize_messages_tuple((chunk, metadata))
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assert result == [{"key": "v2"}, {"langgraph_node": "agent"}]
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def test_serialize_messages_tuple_non_dict_metadata():
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from deerflow.runtime.serialization import serialize_messages_tuple
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result = serialize_messages_tuple((_FakePydanticV2(), "not-a-dict"))
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assert result == [{"key": "v2"}, {}]
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def test_serialize_messages_tuple_fallback():
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from deerflow.runtime.serialization import serialize_messages_tuple
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result = serialize_messages_tuple("not-a-tuple")
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assert result == "not-a-tuple"
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def test_serialize_dispatcher_messages_mode():
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from deerflow.runtime.serialization import serialize
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chunk = _FakePydanticV2()
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result = serialize((chunk, {"node": "x"}), mode="messages")
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assert result == [{"key": "v2"}, {"node": "x"}]
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def test_serialize_dispatcher_values_mode():
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from deerflow.runtime.serialization import serialize
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result = serialize({"msg": "hi", "__pregel_tasks": "x"}, mode="values")
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assert result == {"msg": "hi"}
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def test_serialize_dispatcher_default_mode():
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from deerflow.runtime.serialization import serialize
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result = serialize(_FakePydanticV1())
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assert result == {"key": "v1"}
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# ── non-finite float normalization ───────────────────────────────────────────
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class _FakePydanticWithNonFinite:
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"""Object whose model_dump output carries a nested non-finite float.
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Mirrors a live LangChain message whose ``response_metadata`` holds a
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provider logprob of ``-inf`` for a zero-probability token.
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"""
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def model_dump(self):
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return {"response_metadata": {"logprobs": {"token": float("-inf")}}}
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def test_serialize_finite_floats_unchanged():
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from deerflow.runtime.serialization import serialize_lc_object
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assert serialize_lc_object(3.14) == 3.14
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assert serialize_lc_object([1.5, {"x": -2.0}]) == [1.5, {"x": -2.0}]
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def test_serialize_non_finite_floats_normalized_to_none():
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import math
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from deerflow.runtime.serialization import serialize_lc_object
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assert serialize_lc_object(float("nan")) is None
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assert serialize_lc_object(float("inf")) is None
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assert serialize_lc_object(float("-inf")) is None
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result = serialize_lc_object({"a": float("inf"), "b": [float("nan"), 1.0]})
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assert result == {"a": None, "b": [None, 1.0]}
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assert all(not isinstance(v, float) or math.isfinite(v) for v in _iter_floats(result))
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def test_serialize_recurses_into_model_dump_output():
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"""A non-finite float inside a model_dump result must also be normalized."""
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from deerflow.runtime.serialization import serialize_lc_object
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result = serialize_lc_object(_FakePydanticWithNonFinite())
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assert result == {"response_metadata": {"logprobs": {"token": None}}}
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def test_serialize_channel_values_json_safe_for_both_wires():
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"""Serialized channel values must survive a strict (allow_nan=False) encoder."""
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import json
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from deerflow.runtime.serialization import serialize_channel_values_for_api
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raw = {
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"messages": [{"type": "ai", "content": "hi", "response_metadata": {"logprobs": {"t": float("-inf")}}}],
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"some_metric": float("nan"),
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}
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result = serialize_channel_values_for_api(raw)
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# Strict encoding mirrors Starlette's JSONResponse.render (REST path).
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json.dumps(result, allow_nan=False)
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# Strict decoding mirrors the JS useStream / langgraph-sdk SSE parser.
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encoded = json.dumps(result)
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json.loads(encoded, parse_constant=_reject_constant)
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def test_serialize_messages_mode_json_safe():
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import json
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from deerflow.runtime.serialization import serialize
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# Both halves of the tuple must be JSON-safe: the chunk carries the
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# non-finite in model_dump output, the metadata in caller-supplied run
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# metadata (merged into messages-mode frames by LangGraph).
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payload = serialize(
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(_FakePydanticWithNonFinite(), {"node": "model", "metric": float("-inf")}),
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mode="messages",
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)
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assert payload[1]["metric"] is None
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json.dumps(payload, allow_nan=False)
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json.loads(json.dumps(payload), parse_constant=_reject_constant)
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def test_serialize_messages_tuple_metadata_non_finite_floats_normalized():
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"""The metadata half of a messages-mode tuple rides the same wire as the
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chunk: non-finite floats nested there must be normalized too (#7669), not
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emitted as bare ``NaN`` / ``Infinity`` tokens."""
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import json
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from deerflow.runtime.serialization import serialize_messages_tuple
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metadata = {
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"langgraph_node": "agent",
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"langgraph_step": 1,
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"stats": {"score": float("inf"), "delta": float("nan")},
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}
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result = serialize_messages_tuple(({"content": "hi"}, metadata))
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assert result[1] == {
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"langgraph_node": "agent",
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"langgraph_step": 1,
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"stats": {"score": None, "delta": None},
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}
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# Strict encoding mirrors Starlette's JSONResponse; strict decoding
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# mirrors the JS useStream / langgraph-sdk SSE parser.
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json.dumps(result, allow_nan=False)
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json.loads(json.dumps(result), parse_constant=_reject_constant)
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def test_serialize_messages_mode_json_safe_metadata():
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"""At the dispatcher level metadata gets the same canonical treatment as
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the chunk: tuples become lists (their JSON shape anyway) and nested
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objects are recursively serialized."""
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import json
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from deerflow.runtime.serialization import serialize
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payload = serialize(
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(
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{"content": "hi"},
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{
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"langgraph_node": "agent",
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"langgraph_triggers": ("branch:to:agent",),
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"nested": {"obj": _FakePydanticV2(), "floor": float("-inf")},
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},
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),
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mode="messages",
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)
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assert payload[1]["langgraph_node"] == "agent"
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assert payload[1]["langgraph_triggers"] == ["branch:to:agent"]
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assert payload[1]["nested"] == {"obj": {"key": "v2"}, "floor": None}
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json.dumps(payload, allow_nan=False)
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json.loads(json.dumps(payload), parse_constant=_reject_constant)
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def _reject_constant(token):
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raise ValueError(f"non-finite JSON token: {token}")
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def _iter_floats(obj):
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if isinstance(obj, float):
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yield obj
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elif isinstance(obj, dict):
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for v in obj.values():
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yield from _iter_floats(v)
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elif isinstance(obj, (list, tuple)):
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for v in obj:
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yield from _iter_floats(v)
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# ── strip_data_url_image_blocks ──────────────────────────────────────────────
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def _make_msg(
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content,
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*,
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hide_from_ui=False,
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msg_type="human",
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):
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"""Build a serialised-style message dict."""
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msg = {"type": msg_type, "content": content}
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if hide_from_ui:
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msg["additional_kwargs"] = {"hide_from_ui": True}
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return msg
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def test_strip_data_url_removes_base64_from_hidden_messages():
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from deerflow.runtime.serialization import strip_data_url_image_blocks
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messages = [
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_make_msg(
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[
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{"type": "text", "text": "Here are the images:"},
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{
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"type": "image_url",
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"image_url": {"url": "data:image/png;base64,iVBOR..."},
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},
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{"type": "text", "text": "- file.jpg (image/jpeg)"},
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{
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"type": "image_url",
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"image_url": {"url": "data:image/jpeg;base64,/9j/..."},
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},
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],
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hide_from_ui=True,
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),
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]
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result = strip_data_url_image_blocks(messages)
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assert len(result) == 1
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content = result[0]["content"]
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# Only text blocks remain
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assert content == [
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{"type": "text", "text": "Here are the images:"},
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{"type": "text", "text": "- file.jpg (image/jpeg)"},
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]
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def test_strip_data_url_preserves_non_hidden_messages():
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from deerflow.runtime.serialization import strip_data_url_image_blocks
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messages = [
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_make_msg(
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[
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{"type": "text", "text": "Check this out"},
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{
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"type": "image_url",
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"image_url": {"url": "data:image/png;base64,iVBOR..."},
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},
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],
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hide_from_ui=False,
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),
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]
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result = strip_data_url_image_blocks(messages)
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assert result == messages
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def test_strip_data_url_preserves_https_image_urls():
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from deerflow.runtime.serialization import strip_data_url_image_blocks
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messages = [
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_make_msg(
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[
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{"type": "text", "text": "See image"},
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{
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"type": "image_url",
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"image_url": {"url": "https://example.com/img.png"},
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},
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],
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hide_from_ui=True,
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),
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]
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result = strip_data_url_image_blocks(messages)
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assert result == messages
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def test_strip_data_url_handles_string_content():
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from deerflow.runtime.serialization import strip_data_url_image_blocks
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messages = [
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_make_msg("plain text content", hide_from_ui=True),
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]
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result = strip_data_url_image_blocks(messages)
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assert result == messages
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def test_strip_data_url_handles_non_dict_messages():
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from deerflow.runtime.serialization import strip_data_url_image_blocks
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result = strip_data_url_image_blocks(["a_string", None, 42])
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assert result == ["a_string", None, 42]
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def test_strip_data_url_mixed_messages():
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"""A realistic mix: normal user message + hidden image injection + AI reply."""
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from deerflow.runtime.serialization import strip_data_url_image_blocks
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messages = [
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_make_msg("Please analyze this image", hide_from_ui=False),
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_make_msg(
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[
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{"type": "text", "text": "Here are the images:"},
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{
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"type": "image_url",
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"image_url": {"url": "data:image/png;base64,AABBCCDD"},
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},
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],
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hide_from_ui=True,
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),
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_make_msg("I can see a landscape", msg_type="ai"),
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]
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result = strip_data_url_image_blocks(messages)
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assert len(result) == 3
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# First message untouched
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assert result[0]["content"] == "Please analyze this image"
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# Hidden message: image_url stripped, text kept
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assert result[1]["content"] == [{"type": "text", "text": "Here are the images:"}]
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# AI message untouched
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assert result[2]["content"] == "I can see a landscape"
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def test_serialize_channel_values_for_api_strips_base64():
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from deerflow.runtime.serialization import serialize_channel_values_for_api
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channel_values = {
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"messages": [
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{
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"type": "human",
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"content": "hello",
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},
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{
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"type": "human",
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"content": [
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{"type": "text", "text": "images:"},
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{
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"type": "image_url",
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"image_url": {"url": "data:image/png;base64,BIGDATA"},
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},
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],
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"additional_kwargs": {"hide_from_ui": True},
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},
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],
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"title": "My thread",
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}
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result = serialize_channel_values_for_api(channel_values)
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assert result["title"] == "My thread"
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assert len(result["messages"]) == 2
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assert result["messages"][0]["content"] == "hello"
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# base64 block stripped, text block kept
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assert result["messages"][1]["content"] == [{"type": "text", "text": "images:"}]
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def test_serialize_channel_values_for_api_no_messages():
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"""When channel_values has no messages key, returns without error."""
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from deerflow.runtime.serialization import serialize_channel_values_for_api
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result = serialize_channel_values_for_api({"title": "empty"})
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assert result == {"title": "empty"}
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def test_serialize_values_mode_strips_base64_from_hidden_messages():
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"""The SSE stream emits ``values`` snapshots of the full state, so it must
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strip base64 image data from hide_from_ui messages just like the REST
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endpoints do — otherwise the same payload leaks over the stream."""
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import json
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from deerflow.runtime.serialization import serialize
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state = {
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"messages": [
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_make_msg(
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[
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{"type": "text", "text": "context"},
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{
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"type": "image_url",
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"image_url": {"url": "data:image/png;base64,iVBOR..."},
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},
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],
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hide_from_ui=True,
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),
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],
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}
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result = serialize(state, mode="values")
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# the hidden message survives (count/order preserved) but the data: block is gone
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assert len(result["messages"]) == 1
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assert "data:image/png;base64" not in json.dumps(result)
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assert result["messages"][0]["content"] == [{"type": "text", "text": "context"}]
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