mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-23 16:12:25 +02:00
Sydney RunkleandClaude Sonnet 4.6
a455cefd24
The dict-reducer tests were incorrectly removed — they test valid DeltaChannel behavior (dict type inference via Annotated) that was already supported by the base branch's _is_field_channel logic in state.py. The only thing needed was fixing the module name. Also updates all remaining channels._delta imports to channels.delta across state.py and test files. Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
597 lines
20 KiB
Python
597 lines
20 KiB
Python
import operator
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from collections.abc import Sequence
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import pytest
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from langchain_core.messages import AIMessage, HumanMessage
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from langgraph.checkpoint.base import DELTA_SENTINEL
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from langgraph._internal._typing import MISSING
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from langgraph.channels.binop import BinaryOperatorAggregate
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from langgraph.channels.delta import DeltaChannel
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from langgraph.channels.last_value import LastValue
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from langgraph.channels.topic import Topic
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from langgraph.channels.untracked_value import UntrackedValue
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from langgraph.errors import EmptyChannelError, InvalidUpdateError
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from langgraph.graph.message import add_messages
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pytestmark = pytest.mark.anyio
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def test_last_value() -> None:
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channel = LastValue(int).from_checkpoint(MISSING)
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assert channel.ValueType is int
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assert channel.UpdateType is int
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with pytest.raises(EmptyChannelError):
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channel.get()
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with pytest.raises(InvalidUpdateError):
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channel.update([5, 6])
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channel.update([3])
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assert channel.get() == 3
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channel.update([4])
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assert channel.get() == 4
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checkpoint = channel.checkpoint()
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channel = LastValue(int).from_checkpoint(checkpoint)
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assert channel.get() == 4
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def test_topic() -> None:
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channel = Topic(str).from_checkpoint(MISSING)
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assert channel.ValueType == Sequence[str]
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assert channel.UpdateType == str | list[str]
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assert channel.update(["a", "b"])
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assert channel.get() == ["a", "b"]
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assert channel.update([["c", "d"], "d"])
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assert channel.get() == ["c", "d", "d"]
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assert channel.update([])
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with pytest.raises(EmptyChannelError):
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channel.get()
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assert not channel.update([]), "channel already empty"
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assert channel.update(["e"])
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assert channel.get() == ["e"]
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checkpoint = channel.checkpoint()
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channel = Topic(str).from_checkpoint(checkpoint)
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assert channel.get() == ["e"]
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channel_copy = Topic(str).from_checkpoint(checkpoint)
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channel_copy.update(["f"])
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assert channel_copy.get() == ["f"]
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assert channel.get() == ["e"]
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def test_topic_accumulate() -> None:
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channel = Topic(str, accumulate=True).from_checkpoint(MISSING)
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assert channel.ValueType == Sequence[str]
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assert channel.UpdateType == str | list[str]
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assert channel.update(["a", "b"])
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assert channel.get() == ["a", "b"]
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assert channel.update(["b", ["c", "d"], "d"])
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assert channel.get() == ["a", "b", "b", "c", "d", "d"]
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assert not channel.update([])
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assert channel.get() == ["a", "b", "b", "c", "d", "d"]
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checkpoint = channel.checkpoint()
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channel = Topic(str, accumulate=True).from_checkpoint(checkpoint)
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assert channel.get() == ["a", "b", "b", "c", "d", "d"]
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assert channel.update(["e"])
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assert channel.get() == ["a", "b", "b", "c", "d", "d", "e"]
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def test_binop() -> None:
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channel = BinaryOperatorAggregate(int, operator.add).from_checkpoint(MISSING)
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assert channel.ValueType is int
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assert channel.UpdateType is int
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assert channel.get() == 0
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channel.update([1, 2, 3])
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assert channel.get() == 6
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channel.update([4])
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assert channel.get() == 10
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checkpoint = channel.checkpoint()
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channel = BinaryOperatorAggregate(int, operator.add).from_checkpoint(checkpoint)
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assert channel.get() == 10
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def test_untracked_value() -> None:
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channel = UntrackedValue(dict).from_checkpoint(MISSING)
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assert channel.ValueType is dict
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assert channel.UpdateType is dict
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# UntrackedValue should start empty
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with pytest.raises(EmptyChannelError):
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channel.get()
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# Should be able to update with a value
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test_data = {"session": "test", "temp": "dir"}
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channel.update([test_data])
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assert channel.get() == test_data
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# Update with new value
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new_data = {"session": "updated", "temp": "newdir"}
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channel.update([new_data])
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assert channel.get() == new_data
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# On checkpoint, UntrackedValue should return MISSING
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checkpoint = channel.checkpoint()
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assert checkpoint is MISSING
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# Creating from checkpoint with MISSING should start empty
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new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
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with pytest.raises(EmptyChannelError):
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new_channel.get()
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def test_delta_channel_basic_two_steps() -> None:
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from langchain_core.messages import AIMessage, HumanMessage
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from langgraph.checkpoint.base import DELTA_SENTINEL
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from langgraph.graph.message import add_messages
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ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
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# Step 1: one message added
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ch.update([HumanMessage(content="hi", id="h1")])
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d1 = ch.checkpoint()
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assert d1 is DELTA_SENTINEL
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# Step 2: another message
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ch.update([AIMessage(content="hello", id="a1")])
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d2 = ch.checkpoint()
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assert d2 is DELTA_SENTINEL
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# Full accumulated value is preserved in memory
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assert len(ch.get()) == 2
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assert ch.get()[0].content == "hi"
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assert ch.get()[1].content == "hello"
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def test_delta_channel_from_checkpoint_writes_list() -> None:
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"""replay_writes on a fresh channel replays through the operator."""
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from langchain_core.messages import AIMessage, HumanMessage
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from langgraph.graph.message import add_messages
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spec = DeltaChannel(add_messages)
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ch = spec.from_checkpoint(DELTA_SENTINEL)
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ch.replay_writes(
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[
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("t0", "messages", HumanMessage(content="hi", id="h1")),
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("t1", "messages", AIMessage(content="hello", id="a1")),
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("t2", "messages", HumanMessage(content="bye", id="h2")),
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]
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)
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msgs = ch.get()
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assert len(msgs) == 3
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assert msgs[0].content == "hi"
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assert msgs[1].content == "hello"
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assert msgs[2].content == "bye"
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def test_delta_channel_from_checkpoint_backwards_compat() -> None:
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from langchain_core.messages import HumanMessage
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from langgraph.graph.message import add_messages
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# Old BinaryOperatorAggregate checkpoint: plain list treated as backward compat
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spec = DeltaChannel(add_messages)
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old_value = [HumanMessage(content="old", id="h1")]
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ch = spec.from_checkpoint(old_value)
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assert ch.get() == old_value
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def test_delta_channel_overwrite() -> None:
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from langchain_core.messages import HumanMessage
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from langgraph.checkpoint.base import DELTA_SENTINEL
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from langgraph.graph.message import add_messages
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from langgraph.types import Overwrite
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ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
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ch.update([HumanMessage(content="old", id="h1")])
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ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
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d = ch.checkpoint()
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assert d is DELTA_SENTINEL
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# After overwrite, value is reset to only the new message
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assert len(ch.get()) == 1
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assert ch.get()[0].content == "new"
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def test_delta_channel_remove_message_and_replay() -> None:
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"""RemoveMessage must round-trip correctly when writes are replayed."""
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from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
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from langgraph.graph.message import add_messages
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spec = DeltaChannel(add_messages)
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ch = spec.from_checkpoint(MISSING)
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# Step 1: add two messages
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ch.update([HumanMessage(content="hi", id="h1")])
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ch.update([AIMessage(content="hello", id="a1")])
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assert ch.get() == [
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HumanMessage(content="hi", id="h1"),
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AIMessage(content="hello", id="a1"),
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]
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# Step 2: remove the AI message
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ch.update([RemoveMessage(id="a1")])
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assert ch.get() == [HumanMessage(content="hi", id="h1")]
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# Replay the writes list from scratch — must reproduce the post-remove state
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ch2 = spec.from_checkpoint(DELTA_SENTINEL)
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ch2.replay_writes(
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[
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("t0", "messages", HumanMessage(content="hi", id="h1")),
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("t1", "messages", AIMessage(content="hello", id="a1")),
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("t2", "messages", RemoveMessage(id="a1")),
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]
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)
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assert ch2.get() == [HumanMessage(content="hi", id="h1")]
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def test_delta_channel_update_by_id_and_replay() -> None:
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"""Updating a message by ID must round-trip correctly through writes replay."""
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from langchain_core.messages import HumanMessage
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from langgraph.graph.message import add_messages
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spec = DeltaChannel(add_messages)
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ch = spec.from_checkpoint(MISSING)
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# Step 1: add a message
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ch.update([HumanMessage(content="original", id="h1")])
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# Step 2: update the same message by ID
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ch.update([HumanMessage(content="updated", id="h1")])
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assert ch.get() == [HumanMessage(content="updated", id="h1")]
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# Replay writes — must produce the updated message, not the original
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ch2 = spec.from_checkpoint(DELTA_SENTINEL)
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ch2.replay_writes(
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[
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("t0", "messages", HumanMessage(content="original", id="h1")),
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("t1", "messages", HumanMessage(content="updated", id="h1")),
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]
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)
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assert len(ch2.get()) == 1
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assert ch2.get()[0].content == "updated"
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def test_delta_channel_checkpoint_returns_sentinel() -> None:
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"""checkpoint() always returns DELTA_SENTINEL regardless of state."""
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from langgraph.checkpoint.base import DELTA_SENTINEL
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from langgraph.graph.message import add_messages
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ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
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assert ch.checkpoint() is DELTA_SENTINEL
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from langchain_core.messages import HumanMessage
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ch.update([HumanMessage(content="hi", id="h1")])
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assert ch.checkpoint() is DELTA_SENTINEL
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def test_delta_channel_inmemory_saver_assembles_writes() -> None:
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"""InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
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from typing import Annotated
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from langchain_core.messages import AIMessage, HumanMessage
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from langgraph.checkpoint.memory import InMemorySaver
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from typing_extensions import TypedDict
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from langgraph.graph import START, StateGraph
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from langgraph.graph.message import add_messages
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class State(TypedDict):
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messages: Annotated[list, DeltaChannel(add_messages)]
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n = {"v": 0}
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def respond(state: State) -> dict:
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n["v"] += 1
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return {"messages": [AIMessage(content=f"ok{n['v']}", id=f"ai{n['v']}")]}
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builder = StateGraph(State)
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builder.add_node("respond", respond)
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builder.add_edge(START, "respond")
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saver = InMemorySaver()
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graph = builder.compile(checkpointer=saver)
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config = {"configurable": {"thread_id": "t1"}}
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graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
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graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)
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# get_tuple returns raw storage shape — channel_values stores DELTA_SENTINEL
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# for delta channels; the reconstructed writes flow separately via
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# saver._get_channel_writes_history.
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saved = saver.get_tuple(config)
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assert saved is not None
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assert "messages" in saved.checkpoint["channel_values"]
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assert saved.checkpoint["channel_values"]["messages"] is DELTA_SENTINEL
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state = graph.get_state(config)
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assert len(state.values["messages"]) == 4 # 2 human + 2 AI
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# ---------------------------------------------------------------------------
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# Dict-reducer tests
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# ---------------------------------------------------------------------------
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def _delta_channel_with_type(operator, typ):
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"""Build a DeltaChannel with an explicit type via the Annotated injection path."""
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from typing import Annotated
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from langgraph.channels.delta import DeltaChannel
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from langgraph.graph.state import _get_channel
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return _get_channel("_test", Annotated[typ, DeltaChannel(operator)])
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def test_delta_channel_dict_reducer_fresh_channel() -> None:
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"""DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
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def merge_dicts(left: dict, right: dict) -> dict:
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return {**left, **right}
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ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
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assert ch.is_available()
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assert ch.get() == {}
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def test_delta_channel_dict_reducer_basic_updates() -> None:
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"""DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
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def merge_dicts(left: dict, right: dict) -> dict:
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return {**left, **right}
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ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
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ch.update([{"a": 1}])
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d1 = ch.checkpoint()
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assert d1 is DELTA_SENTINEL
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ch.update([{"b": 2}])
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d2 = ch.checkpoint()
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assert d2 is DELTA_SENTINEL
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assert ch.get() == {"a": 1, "b": 2}
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def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
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"""replay_writes on a fresh channel replays through a dict merge reducer."""
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def merge_dicts(left: dict, right: dict) -> dict:
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return {**left, **right}
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spec = _delta_channel_with_type(merge_dicts, dict)
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ch = spec.from_checkpoint(DELTA_SENTINEL)
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ch.replay_writes(
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[
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("t0", "files", {"a": 1}),
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("t1", "files", {"b": 2}),
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("t2", "files", {"c": 3}),
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]
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)
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assert ch.get() == {"a": 1, "b": 2, "c": 3}
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def test_delta_channel_dict_reducer_with_deletions() -> None:
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"""Dict reducer that treats None values as deletions works end-to-end."""
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def merge_files(left: dict | None, right: dict) -> dict:
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if left is None:
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return {k: v for k, v in right.items() if v is not None}
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result = {**left}
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for k, v in right.items():
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if v is None:
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result.pop(k, None)
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else:
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result[k] = v
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return result
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ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
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ch.update([{"file1.py": "content1", "file2.py": "content2"}])
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ch.update([{"file1.py": None, "file3.py": "content3"}])
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assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
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spec = _delta_channel_with_type(merge_files, dict)
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ch2 = spec.from_checkpoint(DELTA_SENTINEL)
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ch2.replay_writes(
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[
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("t0", "files", {"file1.py": "content1", "file2.py": "content2"}),
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("t1", "files", {"file1.py": None, "file3.py": "content3"}),
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]
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)
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assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
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def test_delta_channel_dict_reducer_overwrite_in_update() -> None:
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"""Overwrite(dict) in update() must preserve dict shape, not coerce to list."""
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from langgraph.types import Overwrite
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def merge_dicts(left: dict, right: dict) -> dict:
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return {**left, **right}
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ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
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ch.update([{"a": 1}])
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ch.update([Overwrite({"b": 2, "c": 3})])
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assert ch.get() == {"b": 2, "c": 3}
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def test_delta_channel_dict_reducer_overwrite_in_writes_replay() -> None:
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"""Overwrite(dict) embedded in replayed writes must reconstruct as dict."""
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from langgraph.types import Overwrite
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def merge_dicts(left: dict, right: dict) -> dict:
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return {**left, **right}
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spec = _delta_channel_with_type(merge_dicts, dict)
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ch = spec.from_checkpoint(DELTA_SENTINEL)
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ch.replay_writes(
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[
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("t0", "files", {"a": 1}),
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("t1", "files", Overwrite({"x": 10, "y": 20})),
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("t2", "files", {"z": 30}),
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]
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)
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assert ch.get() == {"x": 10, "y": 20, "z": 30}
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def test_delta_channel_dict_reducer_with_notrequired_annotation() -> None:
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"""DeltaChannel infers dict type through `Annotated[NotRequired[dict[...]], ch]`."""
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from typing import Annotated
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from typing_extensions import NotRequired
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from langgraph.channels.delta import DeltaChannel
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from langgraph.graph.state import _get_channel
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def merge_dicts(left: dict | None, right: dict) -> dict:
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if left is None:
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return dict(right)
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return {**left, **right}
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annotation = Annotated[NotRequired[dict[str, int]], DeltaChannel(merge_dicts)]
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ch = _get_channel("files", annotation).from_checkpoint(MISSING)
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assert ch.get() == {}
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ch.update([{"a": 1}])
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ch.update([{"b": 2}])
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assert ch.get() == {"a": 1, "b": 2}
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def test_delta_channel_dict_reducer_end_to_end_filesystem() -> None:
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"""End-to-end: graph with dict-reducer (filesystem-style) channel wrapped in DeltaChannel."""
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from typing import Annotated
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from langgraph.checkpoint.memory import InMemorySaver
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from typing_extensions import TypedDict
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from langgraph.channels.delta import DeltaChannel
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from langgraph.graph import START, StateGraph
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def merge_files(left: dict | None, right: dict) -> dict:
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if left is None:
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return {k: v for k, v in right.items() if v is not None}
|
|
result = {**left}
|
|
for k, v in right.items():
|
|
if v is None:
|
|
result.pop(k, None)
|
|
else:
|
|
result[k] = v
|
|
return result
|
|
|
|
class State(TypedDict):
|
|
files: Annotated[dict[str, str], DeltaChannel(merge_files)]
|
|
|
|
turn = {"v": 0}
|
|
|
|
def write_file(state: State) -> dict:
|
|
turn["v"] += 1
|
|
n = turn["v"]
|
|
return {"files": {f"/doc_{n}.txt": f"content for turn {n}"}}
|
|
|
|
builder = StateGraph(State)
|
|
builder.add_node("write_file", write_file)
|
|
builder.add_edge(START, "write_file")
|
|
saver = InMemorySaver()
|
|
graph = builder.compile(checkpointer=saver)
|
|
config = {"configurable": {"thread_id": "fs"}}
|
|
|
|
for _ in range(3):
|
|
graph.invoke({"files": {}}, config)
|
|
|
|
saved = saver.get_tuple(config)
|
|
assert saved is not None
|
|
assert saved.checkpoint["channel_values"]["files"] is DELTA_SENTINEL
|
|
state = graph.get_state(config)
|
|
assert state.values["files"] == {
|
|
"/doc_1.txt": "content for turn 1",
|
|
"/doc_2.txt": "content for turn 2",
|
|
"/doc_3.txt": "content for turn 3",
|
|
}
|
|
|
|
def delete_file(state: State) -> dict:
|
|
return {"files": {"/doc_1.txt": None}}
|
|
|
|
builder2 = StateGraph(State)
|
|
builder2.add_node("write_file", write_file)
|
|
builder2.add_node("delete_file", delete_file)
|
|
builder2.add_edge(START, "write_file")
|
|
builder2.add_edge("write_file", "delete_file")
|
|
turn["v"] = 0
|
|
saver2 = InMemorySaver()
|
|
graph2 = builder2.compile(checkpointer=saver2)
|
|
config2 = {"configurable": {"thread_id": "fs2"}}
|
|
graph2.invoke({"files": {}}, config2)
|
|
state2 = graph2.get_state(config2)
|
|
assert state2.values["files"] == {}
|
|
|
|
|
|
def test_delta_channel_dict_reducer_backwards_compat() -> None:
|
|
"""A pre-DeltaChannel dict checkpoint must load as a dict, not be listified."""
|
|
|
|
def merge_dicts(left: dict, right: dict) -> dict:
|
|
return {**left, **right}
|
|
|
|
spec = _delta_channel_with_type(merge_dicts, dict)
|
|
old_value = {"a": 1, "b": 2}
|
|
ch = spec.from_checkpoint(old_value)
|
|
assert ch.get() == {"a": 1, "b": 2}
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# seed / pre-delta migration
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def test_delta_channel_from_checkpoint_honors_seed() -> None:
|
|
"""A non-sentinel value to from_checkpoint is used as the pre-delta seed.
|
|
|
|
Guards the pre-delta migration path: when the saver's ancestor walk hits
|
|
a pre-DeltaChannel blob it passes it as `seed` so replay reconstructs
|
|
the post-migration state correctly rather than replaying from empty.
|
|
"""
|
|
spec = DeltaChannel(add_messages)
|
|
seed = [HumanMessage(content="pre-delta", id="p1")]
|
|
ch = spec.from_checkpoint(seed)
|
|
ch.replay_writes(
|
|
[
|
|
("t0", "messages", AIMessage(content="delta-1", id="d1")),
|
|
("t1", "messages", HumanMessage(content="delta-2", id="d2")),
|
|
]
|
|
)
|
|
msgs = ch.get()
|
|
assert [m.content for m in msgs] == ["pre-delta", "delta-1", "delta-2"]
|
|
|
|
|
|
def test_delta_channel_from_checkpoint_seed_without_writes() -> None:
|
|
"""Reconstruction at a pre-delta ancestor with no newer deltas returns
|
|
just the seed — the saver's terminator fired immediately."""
|
|
spec = DeltaChannel(add_messages)
|
|
seed = [HumanMessage(content="only-snap", id="s1")]
|
|
ch = spec.from_checkpoint(seed)
|
|
ch.replay_writes([])
|
|
assert ch.get() == seed
|
|
|
|
|
|
def test_delta_channel_from_checkpoint_seed_none_is_distinct_from_sentinel() -> None:
|
|
"""`seed=None` must start replay from None, not from an empty channel.
|
|
|
|
The DELTA_SENTINEL / MISSING sentinels mean 'no seed'; passing `None`
|
|
explicitly should feed None to the reducer as the left operand.
|
|
"""
|
|
|
|
def replace(left, right):
|
|
return right
|
|
|
|
spec = DeltaChannel(replace)
|
|
ch = spec.from_checkpoint(None)
|
|
ch.replay_writes([("t0", "x", "after")])
|
|
# Reducer replaces; seed=None → first write produces "after".
|
|
assert ch.get() == "after"
|