mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-30 19:59:40 +02:00
165 lines
5.8 KiB
Python
165 lines
5.8 KiB
Python
from collections import defaultdict
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from functools import partial
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from inspect import signature
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from typing import Any, Optional, Sequence, Type
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from langchain_core.runnables import RunnableLambda, RunnablePassthrough
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from langchain_core.runnables.base import RunnableLike
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from langgraph.channels.base import BaseChannel, InvalidUpdateError
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from langgraph.channels.binop import BinaryOperatorAggregate
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from langgraph.channels.last_value import LastValue
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from langgraph.checkpoint import BaseCheckpointSaver
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from langgraph.graph.graph import END, Graph
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from langgraph.pregel import Channel, Pregel
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from langgraph.pregel.read import ChannelRead
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from langgraph.pregel.write import SKIP_WRITE, ChannelWrite
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START = "__start__"
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class StateGraph(Graph):
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def __init__(self, schema: Type[Any]) -> None:
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super().__init__()
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self.schema = schema
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self.channels = _get_channels(schema)
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if any(isinstance(c, BinaryOperatorAggregate) for c in self.channels.values()):
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self.support_multiple_edges = True
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def add_node(self, key: str, action: RunnableLike) -> None:
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if key in self.channels:
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raise ValueError(
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f"'{key}' is already being used as a state attribute "
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"(a.k.a. a channel), cannot also be used as a node name."
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)
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return super().add_node(key, action)
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def compile(
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self,
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checkpointer: Optional[BaseCheckpointSaver] = None,
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interrupt_before: Optional[Sequence[str]] = None,
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interrupt_after: Optional[Sequence[str]] = None,
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) -> Pregel:
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interrupt_before = interrupt_before or []
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interrupt_after = interrupt_after or []
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self.validate(interrupt=interrupt_before + interrupt_after)
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state_keys = list(self.channels)
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state_keys_read = state_keys[0] if state_keys == ["__root__"] else state_keys
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update_channels = (
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[("__root__", None, True)]
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if not isinstance(state_keys_read, list)
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else [
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(key, RunnableLambda(partial(_dict_getter, state_keys, key)), False)
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for key in state_keys_read
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]
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)
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coerce_state = (
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partial(_coerce_state, self.schema)
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if isinstance(state_keys_read, list)
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else RunnablePassthrough()
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)
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outgoing_edges = defaultdict(list)
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for start, end in self.edges:
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outgoing_edges[start].append(f"{end}:inbox" if end != END else END)
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nodes = {
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key: (
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Channel.subscribe_to(f"{key}:inbox")
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| coerce_state # coerce/validate using schema
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| node
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| ChannelWrite(channels=[(key, None, False)] + update_channels)
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)
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for key, node in self.nodes.items()
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}
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for key in self.nodes:
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outgoing = outgoing_edges[key]
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edges_key = f"{key}:edges"
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if outgoing or key in self.branches:
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nodes[edges_key] = Channel.subscribe_to(
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key, tags=["langsmith:hidden"]
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) | ChannelRead(state_keys_read)
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if outgoing:
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nodes[edges_key] |= Channel.write_to(*[dest for dest in outgoing])
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if key in self.branches:
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for branch in self.branches[key]:
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nodes[edges_key] |= RunnableLambda(
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branch.runnable, name=f"{key}_condition"
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)
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nodes[START] = Channel.subscribe_to(
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f"{START}:inbox", tags=["langsmith:hidden"]
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) | ChannelWrite(channels=[(START, None, False)] + update_channels)
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nodes[f"{START}:edges"] = (
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Channel.subscribe_to(START, tags=["langsmith:hidden"])
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| ChannelRead(state_keys_read)
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| Channel.write_to(f"{self.entry_point}:inbox")
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)
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return Pregel(
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nodes=nodes,
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channels=self.channels,
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input=f"{START}:inbox",
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output=END,
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hidden=[f"{node}:inbox" for node in self.nodes] + [START] + state_keys,
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checkpointer=checkpointer,
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interrupt=(
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[f"{node}:inbox" for node in interrupt_before]
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+ [node for node in interrupt_after]
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),
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)
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def _coerce_state(schema: Type[Any], input: dict[str, Any]) -> dict[str, Any]:
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return schema(**input)
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def _dict_getter(allowed_keys: list[str], key: str, input: dict) -> Any:
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if input is not None:
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if not isinstance(input, dict) or any(key not in allowed_keys for key in input):
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raise InvalidUpdateError(
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f"Invalid state update,"
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f" expected dict with one or more of {allowed_keys}, got {input}"
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)
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return input.get(key, SKIP_WRITE)
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else:
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return SKIP_WRITE
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def _get_channels(schema: Type[dict]) -> dict[str, BaseChannel]:
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if not hasattr(schema, "__annotations__"):
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return {
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"__root__": _get_channel(schema),
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}
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channels: dict[str, BaseChannel] = {}
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for name, typ in schema.__annotations__.items():
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channels[name] = _get_channel(typ)
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return channels
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def _get_channel(annotation: Any) -> Optional[BaseChannel]:
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if channel := _is_field_binop(annotation):
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return channel
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return LastValue(annotation)
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def _is_field_binop(typ: Type[Any]) -> Optional[BinaryOperatorAggregate]:
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if hasattr(typ, "__metadata__"):
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meta = typ.__metadata__
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if len(meta) == 1 and callable(meta[0]):
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sig = signature(meta[0])
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params = list(sig.parameters.values())
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if len(params) == 2 and len(
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[
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p
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for p in params
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if p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD)
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]
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):
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return BinaryOperatorAggregate(typ, meta[0])
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return None
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