MCP Wikipedia Server

from __future__ import annotations import inspect import logging import sys import typing import warnings from collections import defaultdict from collections.abc import Awaitable, Hashable, Sequence from functools import partial from inspect import isclass, isfunction, ismethod, signature from types import FunctionType from typing import ( Any, Callable, Generic, Literal, Union, cast, get_args, get_origin, get_type_hints, overload, ) from langchain_core.runnables import Runnable, RunnableConfig from pydantic import BaseModel, TypeAdapter from typing_extensions import Self, Unpack, is_typeddict from langgraph._internal._constants import ( INTERRUPT, NS_END, NS_SEP, TASKS, ) from langgraph._internal._fields import ( get_cached_annotated_keys, get_field_default, get_update_as_tuples, ) from langgraph._internal._pydantic import create_model from langgraph._internal._runnable import coerce_to_runnable from langgraph._internal._typing import EMPTY_SEQ, MISSING, DeprecatedKwargs from langgraph.cache.base import BaseCache from langgraph.channels.base import BaseChannel from langgraph.channels.binop import BinaryOperatorAggregate from langgraph.channels.ephemeral_value import EphemeralValue from langgraph.channels.last_value import LastValue, LastValueAfterFinish from langgraph.channels.named_barrier_value import ( NamedBarrierValue, NamedBarrierValueAfterFinish, ) from langgraph.checkpoint.base import Checkpoint from langgraph.constants import END, START, TAG_HIDDEN from langgraph.errors import ( ErrorCode, InvalidUpdateError, ParentCommand, create_error_message, ) from langgraph.graph._branch import BranchSpec from langgraph.graph._node import StateNode, StateNodeSpec from langgraph.managed.base import ( ManagedValueSpec, is_managed_value, ) from langgraph.pregel import Pregel from langgraph.pregel._read import ChannelRead, PregelNode from langgraph.pregel._write import ( ChannelWrite, ChannelWriteEntry, ChannelWriteTupleEntry, ) from langgraph.store.base import BaseStore from langgraph.types import ( All, CachePolicy, Checkpointer, Command, RetryPolicy, Send, ) from langgraph.typing import ContextT, InputT, NodeInputT, OutputT, StateT from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10 if sys.version_info < (3, 10): NoneType = type(None) else: from types import NoneType as NoneType __all__ = ("StateGraph", "CompiledStateGraph") logger = logging.getLogger(__name__) _CHANNEL_BRANCH_TO = "branch:to:{}" def _warn_invalid_state_schema(schema: type[Any] | Any) -> None: if isinstance(schema, type): return if typing.get_args(schema): return warnings.warn( f"Invalid state_schema: {schema}. Expected a type or Annotated[type, reducer]. " "Please provide a valid schema to ensure correct updates.\n" " See: https://langchain-ai.github.io/langgraph/reference/graphs/#stategraph" ) def _get_node_name(node: StateNode[Any, ContextT]) -> str: try: return getattr(node, "__name__", node.__class__.__name__) except AttributeError: raise TypeError(f"Unsupported node type: {type(node)}") class StateGraph(Generic[StateT, ContextT, InputT, OutputT]): """A graph whose nodes communicate by reading and writing to a shared state. The signature of each node is State -> Partial<State>. Each state key can optionally be annotated with a reducer function that will be used to aggregate the values of that key received from multiple nodes. The signature of a reducer function is (Value, Value) -> Value. Args: state_schema: The schema class that defines the state. context_schema: The schema class that defines the runtime context. Use this to expose immutable context data to your nodes, like user_id, db_conn, etc. input_schema: The schema class that defines the input to the graph. output_schema: The schema class that defines the output from the graph. !!! warning "`config_schema` Deprecated" The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0. Please use `context_schema` instead to specify the schema for run-scoped context. Example: ```python from langchain_core.runnables import RunnableConfig from typing_extensions import Annotated, TypedDict from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph import StateGraph from langgraph.runtime import Runtime def reducer(a: list, b: int | None) -> list: if b is not None: return a + [b] return a class State(TypedDict): x: Annotated[list, reducer] class Context(TypedDict): r: float graph = StateGraph(state_schema=State, context_schema=Context) def node(state: State, runtime: Runtime[Context]) -> dict: r = runtie.context.get("r", 1.0) x = state["x"][-1] next_value = x * r * (1 - x) return {"x": next_value} graph.add_node("A", node) graph.set_entry_point("A") graph.set_finish_point("A") compiled = graph.compile() step1 = compiled.invoke({"x": 0.5}, context={"r": 3.0}) # {'x': [0.5, 0.75]} ``` """ edges: set[tuple[str, str]] nodes: dict[str, StateNodeSpec[Any, ContextT]] branches: defaultdict[str, dict[str, BranchSpec]] channels: dict[str, BaseChannel] managed: dict[str, ManagedValueSpec] schemas: dict[type[Any], dict[str, BaseChannel | ManagedValueSpec]] waiting_edges: set[tuple[tuple[str, ...], str]] compiled: bool state_schema: type[StateT] context_schema: type[ContextT] | None input_schema: type[InputT] output_schema: type[OutputT] def __init__( self, state_schema: type[StateT], context_schema: type[ContextT] | None = None, *, input_schema: type[InputT] | None = None, output_schema: type[OutputT] | None = None, **kwargs: Unpack[DeprecatedKwargs], ) -> None: if (config_schema := kwargs.get("config_schema", MISSING)) is not MISSING: warnings.warn( "`config_schema` is deprecated and will be removed. Please use `context_schema` instead.", category=LangGraphDeprecatedSinceV10, stacklevel=2, ) if context_schema is None: context_schema = cast(type[ContextT], config_schema) if (input_ := kwargs.get("input", MISSING)) is not MISSING: warnings.warn( "`input` is deprecated and will be removed. Please use `input_schema` instead.", category=LangGraphDeprecatedSinceV05, stacklevel=2, ) if input_schema is None: input_schema = cast(type[InputT], input_) if (output := kwargs.get("output", MISSING)) is not MISSING: warnings.warn( "`output` is deprecated and will be removed. Please use `output_schema` instead.", category=LangGraphDeprecatedSinceV05, stacklevel=2, ) if output_schema is None: output_schema = cast(type[OutputT], output) self.nodes = {} self.edges = set() self.branches = defaultdict(dict) self.schemas = {} self.channels = {} self.managed = {} self.compiled = False self.waiting_edges = set() self.state_schema = state_schema self.input_schema = cast(type[InputT], input_schema or state_schema) self.output_schema = cast(type[OutputT], output_schema or state_schema) self.context_schema = context_schema self._add_schema(self.state_schema) self._add_schema(self.input_schema, allow_managed=False) self._add_schema(self.output_schema, allow_managed=False) @property def _all_edges(self) -> set[tuple[str, str]]: return self.edges | { (start, end) for starts, end in self.waiting_edges for start in starts } def _add_schema(self, schema: type[Any], /, allow_managed: bool = True) -> None: if schema not in self.schemas: _warn_invalid_state_schema(schema) channels, managed, type_hints = _get_channels(schema) if managed and not allow_managed: names = ", ".join(managed) schema_name = getattr(schema, "__name__", "") raise ValueError( f"Invalid managed channels detected in {schema_name}: {names}." " Managed channels are not permitted in Input/Output schema." ) self.schemas[schema] = {**channels, **managed} for key, channel in channels.items(): if key in self.channels: if self.channels[key] != channel: if isinstance(channel, LastValue): pass else: raise ValueError( f"Channel '{key}' already exists with a different type" ) else: self.channels[key] = channel for key, managed in managed.items(): if key in self.managed: if self.managed[key] != managed: raise ValueError( f"Managed value '{key}' already exists with a different type" ) else: self.managed[key] = managed @overload def add_node( self, node: StateNode[NodeInputT, ContextT], *, defer: bool = False, metadata: dict[str, Any] | None = None, input_schema: None = None, retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None, cache_policy: CachePolicy | None = None, destinations: dict[str, str] | tuple[str, ...] | None = None, **kwargs: Unpack[DeprecatedKwargs], ) -> Self: """Add a new node to the state graph, input schema is inferred as the state schema. Will take the name of the function/runnable as the node name. """ ... @overload def add_node( self, node: StateNode[NodeInputT, ContextT], *, defer: bool = False, metadata: dict[str, Any] | None = None, input_schema: type[NodeInputT], retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None, cache_policy: CachePolicy | None = None, destinations: dict[str, str] | tuple[str, ...] | None = None, **kwargs: Unpack[DeprecatedKwargs], ) -> Self: """Add a new node to the state graph, input schema is specified. Will take the name of the function/runnable as the node name. """ ... @overload def add_node( self, node: str, action: StateNode[NodeInputT, ContextT], *, defer: bool = False, metadata: dict[str, Any] | None = None, input_schema: None = None, retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None, cache_policy: CachePolicy | None = None, destinations: dict[str, str] | tuple[str, ...] | None = None, **kwargs: Unpack[DeprecatedKwargs], ) -> Self: """Add a new node to the state graph, input schema is inferred as the state schema.""" ... @overload def add_node( self, node: str | StateNode[NodeInputT, ContextT], action: StateNode[NodeInputT, ContextT] | None = None, *, defer: bool = False, metadata: dict[str, Any] | None = None, input_schema: type[NodeInputT], retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None, cache_policy: CachePolicy | None = None, destinations: dict[str, str] | tuple[str, ...] | None = None, **kwargs: Unpack[DeprecatedKwargs], ) -> Self: """Add a new node to the state graph, input schema is specified.""" ... def add_node( self, node: str | StateNode[NodeInputT, ContextT], action: StateNode[NodeInputT, ContextT] | None = None, *, defer: bool = False, metadata: dict[str, Any] | None = None, input_schema: type[NodeInputT] | None = None, retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None, cache_policy: CachePolicy | None = None, destinations: dict[str, str] | tuple[str, ...] | None = None, **kwargs: Unpack[DeprecatedKwargs], ) -> Self: """Add a new node to the state graph. Args: node: The function or runnable this node will run. If a string is provided, it will be used as the node name, and action will be used as the function or runnable. action: The action associated with the node. (default: None) Will be used as the node function or runnable if `node` is a string (node name). defer: Whether to defer the execution of the node until the run is about to end. metadata: The metadata associated with the node. (default: None) input_schema: The input schema for the node. (default: the graph's state schema) retry_policy: The retry policy for the node. (default: None) If a sequence is provided, the first matching policy will be applied. cache_policy: The cache policy for the node. (default: None) destinations: Destinations that indicate where a node can route to. This is useful for edgeless graphs with nodes that return `Command` objects. If a dict is provided, the keys will be used as the target node names and the values will be used as the labels for the edges. If a tuple is provided, the values will be used as the target node names. NOTE: this is only used for graph rendering and doesn't have any effect on the graph execution. Example: ```python from typing_extensions import TypedDict from langchain_core.runnables import RunnableConfig from langgraph.graph import START, StateGraph class State(TypedDict): x: int def my_node(state: State, config: RunnableConfig) -> State: return {"x": state["x"] + 1} builder = StateGraph(State) builder.add_node(my_node) # node name will be 'my_node' builder.add_edge(START, "my_node") graph = builder.compile() graph.invoke({"x": 1}) # {'x': 2} ``` Example: Customize the name: ```python builder = StateGraph(State) builder.add_node("my_fair_node", my_node) builder.add_edge(START, "my_fair_node") graph = builder.compile() graph.invoke({"x": 1}) # {'x': 2} ``` Returns: Self: The instance of the state graph, allowing for method chaining. """ if (retry := kwargs.get("retry", MISSING)) is not MISSING: warnings.warn( "`retry` is deprecated and will be removed. Please use `retry_policy` instead.", category=LangGraphDeprecatedSinceV05, ) if retry_policy is None: retry_policy = retry # type: ignore[assignment] if (input_ := kwargs.get("input", MISSING)) is not MISSING: warnings.warn( "`input` is deprecated and will be removed. Please use `input_schema` instead.", category=LangGraphDeprecatedSinceV05, ) if input_schema is None: input_schema = cast(Union[type[NodeInputT], None], input_) if not isinstance(node, str): action = node if isinstance(action, Runnable): node = action.get_name() else: node = getattr(action, "__name__", action.__class__.__name__) if node is None: raise ValueError( "Node name must be provided if action is not a function" ) if self.compiled: logger.warning( "Adding a node to a graph that has already been compiled. This will " "not be reflected in the compiled graph." ) if not isinstance(node, str): action = node node = cast(str, getattr(action, "name", getattr(action, "__name__", None))) if node is None: raise ValueError( "Node name must be provided if action is not a function" ) if action is None: raise RuntimeError if node in self.nodes: raise ValueError(f"Node `{node}` already present.") if node == END or node == START: raise ValueError(f"Node `{node}` is reserved.") for character in (NS_SEP, NS_END): if character in node: raise ValueError( f"'{character}' is a reserved character and is not allowed in the node names." ) inferred_input_schema = None ends: tuple[str, ...] | dict[str, str] = EMPTY_SEQ try: if ( isfunction(action) or ismethod(action) or ismethod(getattr(action, "__call__", None)) ) and ( hints := get_type_hints(getattr(action, "__call__")) or get_type_hints(action) ): if input_schema is None: first_parameter_name = next( iter( inspect.signature( cast(FunctionType, action) ).parameters.keys() ) ) if input_hint := hints.get(first_parameter_name): if isinstance(input_hint, type) and get_type_hints(input_hint): inferred_input_schema = input_hint if rtn := hints.get("return"): # Handle Union types rtn_origin = get_origin(rtn) if rtn_origin is Union: rtn_args = get_args(rtn) # Look for Command in the union for arg in rtn_args: arg_origin = get_origin(arg) if arg_origin is Command: rtn = arg rtn_origin = arg_origin break # Check if it's a Command type if ( rtn_origin is Command and (rargs := get_args(rtn)) and get_origin(rargs[0]) is Literal and (vals := get_args(rargs[0])) ): ends = vals except (NameError, TypeError, StopIteration): pass if destinations is not None: ends = destinations if input_schema is not None: self.nodes[node] = StateNodeSpec[NodeInputT, ContextT]( coerce_to_runnable(action, name=node, trace=False), # type: ignore[arg-type] metadata, input_schema=input_schema, retry_policy=retry_policy, cache_policy=cache_policy, ends=ends, defer=defer, ) elif inferred_input_schema is not None: self.nodes[node] = StateNodeSpec( coerce_to_runnable(action, name=node, trace=False), # type: ignore[arg-type] metadata, input_schema=inferred_input_schema, retry_policy=retry_policy, cache_policy=cache_policy, ends=ends, defer=defer, ) else: self.nodes[node] = StateNodeSpec[StateT, ContextT]( coerce_to_runnable(action, name=node, trace=False), # type: ignore[arg-type] metadata, input_schema=self.state_schema, retry_policy=retry_policy, cache_policy=cache_policy, ends=ends, defer=defer, ) input_schema = input_schema or inferred_input_schema if input_schema is not None: self._add_schema(input_schema) return self def add_edge(self, start_key: str | list[str], end_key: str) -> Self: """Add a directed edge from the start node (or list of start nodes) to the end node. When a single start node is provided, the graph will wait for that node to complete before executing the end node. When multiple start nodes are provided, the graph will wait for ALL of the start nodes to complete before executing the end node. Args: start_key: The key(s) of the start node(s) of the edge. end_key: The key of the end node of the edge. Raises: ValueError: If the start key is 'END' or if the start key or end key is not present in the graph. Returns: Self: The instance of the state graph, allowing for method chaining. """ if self.compiled: logger.warning( "Adding an edge to a graph that has already been compiled. This will " "not be reflected in the compiled graph." ) if isinstance(start_key, str): if start_key == END: raise ValueError("END cannot be a start node") if end_key == START: raise ValueError("START cannot be an end node") # run this validation only for non-StateGraph graphs if not hasattr(self, "channels") and start_key in set( start for start, _ in self.edges ): raise ValueError( f"Already found path for node '{start_key}'.\n" "For multiple edges, use StateGraph with an Annotated state key." ) self.edges.add((start_key, end_key)) return self for start in start_key: if start == END: raise ValueError("END cannot be a start node") if start not in self.nodes: raise ValueError(f"Need to add_node `{start}` first") if end_key == START: raise ValueError("START cannot be an end node") if end_key != END and end_key not in self.nodes: raise ValueError(f"Need to add_node `{end_key}` first") self.waiting_edges.add((tuple(start_key), end_key)) return self def add_conditional_edges( self, source: str, path: Callable[..., Hashable | list[Hashable]] | Callable[..., Awaitable[Hashable | list[Hashable]]] | Runnable[Any, Hashable | list[Hashable]], path_map: dict[Hashable, str] | list[str] | None = None, ) -> Self: """Add a conditional edge from the starting node to any number of destination nodes. Args: source: The starting node. This conditional edge will run when exiting this node. path: The callable that determines the next node or nodes. If not specifying `path_map` it should return one or more nodes. If it returns END, the graph will stop execution. path_map: Optional mapping of paths to node names. If omitted the paths returned by `path` should be node names. Returns: Self: The instance of the graph, allowing for method chaining. Note: Without typehints on the `path` function's return value (e.g., `-> Literal["foo", "__end__"]:`) or a path_map, the graph visualization assumes the edge could transition to any node in the graph. """ # noqa: E501 if self.compiled: logger.warning( "Adding an edge to a graph that has already been compiled. This will " "not be reflected in the compiled graph." ) # find a name for the condition path = coerce_to_runnable(path, name=None, trace=True) name = path.name or "condition" # validate the condition if name in self.branches[source]: raise ValueError( f"Branch with name `{path.name}` already exists for node `{source}`" ) # save it self.branches[source][name] = BranchSpec.from_path(path, path_map, True) if schema := self.branches[source][name].input_schema: self._add_schema(schema) return self def add_sequence( self, nodes: Sequence[ StateNode[NodeInputT, ContextT] | tuple[str, StateNode[NodeInputT, ContextT]] ], ) -> Self: """Add a sequence of nodes that will be executed in the provided order. Args: nodes: A sequence of StateNodes (callables that accept a state arg) or (name, StateNode) tuples. If no names are provided, the name will be inferred from the node object (e.g. a runnable or a callable name). Each node will be executed in the order provided. Raises: ValueError: if the sequence is empty. ValueError: if the sequence contains duplicate node names. Returns: Self: The instance of the state graph, allowing for method chaining. """ if len(nodes) < 1: raise ValueError("Sequence requires at least one node.") previous_name: str | None = None for node in nodes: if isinstance(node, tuple) and len(node) == 2: name, node = node else: name = _get_node_name(node) if name in self.nodes: raise ValueError( f"Node names must be unique: node with the name '{name}' already exists. " "If you need to use two different runnables/callables with the same name (for example, using `lambda`), please provide them as tuples (name, runnable/callable)." ) self.add_node(name, node) if previous_name is not None: self.add_edge(previous_name, name) previous_name = name return self def set_entry_point(self, key: str) -> Self: """Specifies the first node to be called in the graph. Equivalent to calling `add_edge(START, key)`. Parameters: key (str): The key of the node to set as the entry point. Returns: Self: The instance of the graph, allowing for method chaining. """ return self.add_edge(START, key) def set_conditional_entry_point( self, path: Callable[..., Hashable | list[Hashable]] | Callable[..., Awaitable[Hashable | list[Hashable]]] | Runnable[Any, Hashable | list[Hashable]], path_map: dict[Hashable, str] | list[str] | None = None, ) -> Self: """Sets a conditional entry point in the graph. Args: path: The callable that determines the next node or nodes. If not specifying `path_map` it should return one or more nodes. If it returns END, the graph will stop execution. path_map: Optional mapping of paths to node names. If omitted the paths returned by `path` should be node names. Returns: Self: The instance of the graph, allowing for method chaining. """ return self.add_conditional_edges(START, path, path_map) def set_finish_point(self, key: str) -> Self: """Marks a node as a finish point of the graph. If the graph reaches this node, it will cease execution. Parameters: key (str): The key of the node to set as the finish point. Returns: Self: The instance of the graph, allowing for method chaining. """ return self.add_edge(key, END) def validate(self, interrupt: Sequence[str] | None = None) -> Self: # assemble sources all_sources = {src for src, _ in self._all_edges} for start, branches in self.branches.items(): all_sources.add(start) for name, spec in self.nodes.items(): if spec.ends: all_sources.add(name) # validate sources for source in all_sources: if source not in self.nodes and source != START: raise ValueError(f"Found edge starting at unknown node '{source}'") if START not in all_sources: raise ValueError( "Graph must have an entrypoint: add at least one edge from START to another node" ) # assemble targets all_targets = {end for _, end in self._all_edges} for start, branches in self.branches.items(): for cond, branch in branches.items(): if branch.ends is not None: for end in branch.ends.values(): if end not in self.nodes and end != END: raise ValueError( f"At '{start}' node, '{cond}' branch found unknown target '{end}'" ) all_targets.add(end) else: all_targets.add(END) for node in self.nodes: if node != start: all_targets.add(node) for name, spec in self.nodes.items(): if spec.ends: all_targets.update(spec.ends) for target in all_targets: if target not in self.nodes and target != END: raise ValueError(f"Found edge ending at unknown node `{target}`") # validate interrupts if interrupt: for node in interrupt: if node not in self.nodes: raise ValueError(f"Interrupt node `{node}` not found") self.compiled = True return self def compile( self, checkpointer: Checkpointer = None, *, cache: BaseCache | None = None, store: BaseStore | None = None, interrupt_before: All | list[str] | None = None, interrupt_after: All | list[str] | None = None, debug: bool = False, name: str | None = None, ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]: """Compiles the state graph into a `CompiledStateGraph` object. The compiled graph implements the `Runnable` interface and can be invoked, streamed, batched, and run asynchronously. Args: checkpointer: A checkpoint saver object or flag. If provided, this Checkpointer serves as a fully versioned "short-term memory" for the graph, allowing it to be paused, resumed, and replayed from any point. If None, it may inherit the parent graph's checkpointer when used as a subgraph. If False, it will not use or inherit any checkpointer. interrupt_before: An optional list of node names to interrupt before. interrupt_after: An optional list of node names to interrupt after. debug: A flag indicating whether to enable debug mode. name: The name to use for the compiled graph. Returns: CompiledStateGraph: The compiled state graph. """ # assign default values interrupt_before = interrupt_before or [] interrupt_after = interrupt_after or [] # validate the graph self.validate( interrupt=( (interrupt_before if interrupt_before != "*" else []) + interrupt_after if interrupt_after != "*" else [] ) ) # prepare output channels output_channels = ( "__root__" if len(self.schemas[self.output_schema]) == 1 and "__root__" in self.schemas[self.output_schema] else [ key for key, val in self.schemas[self.output_schema].items() if not is_managed_value(val) ] ) stream_channels = ( "__root__" if len(self.channels) == 1 and "__root__" in self.channels else [ key for key, val in self.channels.items() if not is_managed_value(val) ] ) compiled = CompiledStateGraph[StateT, ContextT, InputT, OutputT]( builder=self, schema_to_mapper={}, context_schema=self.context_schema, nodes={}, channels={ **self.channels, **self.managed, START: EphemeralValue(self.input_schema), }, input_channels=START, stream_mode="updates", output_channels=output_channels, stream_channels=stream_channels, checkpointer=checkpointer, interrupt_before_nodes=interrupt_before, interrupt_after_nodes=interrupt_after, auto_validate=False, debug=debug, store=store, cache=cache, name=name or "LangGraph", ) compiled.attach_node(START, None) for key, node in self.nodes.items(): compiled.attach_node(key, node) for start, end in self.edges: compiled.attach_edge(start, end) for starts, end in self.waiting_edges: compiled.attach_edge(starts, end) for start, branches in self.branches.items(): for name, branch in branches.items(): compiled.attach_branch(start, name, branch) return compiled.validate() class CompiledStateGraph( Pregel[StateT, ContextT, InputT, OutputT], Generic[StateT, ContextT, InputT, OutputT], ): builder: StateGraph[StateT, ContextT, InputT, OutputT] schema_to_mapper: dict[type[Any], Callable[[Any], Any] | None] def __init__( self, *, builder: StateGraph[StateT, ContextT, InputT, OutputT], schema_to_mapper: dict[type[Any], Callable[[Any], Any] | None], **kwargs: Any, ) -> None: super().__init__(**kwargs) self.builder = builder self.schema_to_mapper = schema_to_mapper def get_input_jsonschema( self, config: RunnableConfig | None = None ) -> dict[str, Any]: return _get_json_schema( typ=self.builder.input_schema, schemas=self.builder.schemas, channels=self.builder.channels, name=self.get_name("Input"), ) def get_output_jsonschema( self, config: RunnableConfig | None = None ) -> dict[str, Any]: return _get_json_schema( typ=self.builder.output_schema, schemas=self.builder.schemas, channels=self.builder.channels, name=self.get_name("Output"), ) def attach_node(self, key: str, node: StateNodeSpec[Any, ContextT] | None) -> None: if key == START: output_keys = [ k for k, v in self.builder.schemas[self.builder.input_schema].items() if not is_managed_value(v) ] else: output_keys = list(self.builder.channels) + [ k for k, v in self.builder.managed.items() ] def _get_updates( input: None | dict | Any, ) -> Sequence[tuple[str, Any]] | None: if input is None: return None elif isinstance(input, dict): return [(k, v) for k, v in input.items() if k in output_keys] elif isinstance(input, Command): if input.graph == Command.PARENT: return None return [ (k, v) for k, v in input._update_as_tuples() if k in output_keys ] elif ( isinstance(input, (list, tuple)) and input and any(isinstance(i, Command) for i in input) ): updates: list[tuple[str, Any]] = [] for i in input: if isinstance(i, Command): if i.graph == Command.PARENT: continue updates.extend( (k, v) for k, v in i._update_as_tuples() if k in output_keys ) else: updates.extend(_get_updates(i) or ()) return updates elif (t := type(input)) and get_cached_annotated_keys(t): return get_update_as_tuples(input, output_keys) else: msg = create_error_message( message=f"Expected dict, got {input}", error_code=ErrorCode.INVALID_GRAPH_NODE_RETURN_VALUE, ) raise InvalidUpdateError(msg) # state updaters write_entries: tuple[ChannelWriteEntry | ChannelWriteTupleEntry, ...] = ( ChannelWriteTupleEntry( mapper=_get_root if output_keys == ["__root__"] else _get_updates ), ChannelWriteTupleEntry( mapper=_control_branch, static=_control_static(node.ends) if node is not None and node.ends is not None else None, ), ) # add node and output channel if key == START: self.nodes[key] = PregelNode( tags=[TAG_HIDDEN], triggers=[START], channels=START, writers=[ChannelWrite(write_entries)], ) elif node is not None: input_schema = node.input_schema if node else self.builder.state_schema input_channels = list(self.builder.schemas[input_schema]) is_single_input = len(input_channels) == 1 and "__root__" in input_channels if input_schema in self.schema_to_mapper: mapper = self.schema_to_mapper[input_schema] else: mapper = _pick_mapper(input_channels, input_schema) self.schema_to_mapper[input_schema] = mapper branch_channel = _CHANNEL_BRANCH_TO.format(key) self.channels[branch_channel] = ( LastValueAfterFinish(Any) if node.defer else EphemeralValue(Any, guard=False) ) self.nodes[key] = PregelNode( triggers=[branch_channel], # read state keys and managed values channels=("__root__" if is_single_input else input_channels), # coerce state dict to schema class (eg. pydantic model) mapper=mapper, # publish to state keys writers=[ChannelWrite(write_entries)], metadata=node.metadata, retry_policy=node.retry_policy, cache_policy=node.cache_policy, bound=node.runnable, # type: ignore[arg-type] ) else: raise RuntimeError def attach_edge(self, starts: str | Sequence[str], end: str) -> None: if isinstance(starts, str): # subscribe to start channel if end != END: self.nodes[starts].writers.append( ChannelWrite( (ChannelWriteEntry(_CHANNEL_BRANCH_TO.format(end), None),) ) ) elif end != END: channel_name = f"join:{'+'.join(starts)}:{end}" # register channel if self.builder.nodes[end].defer: self.channels[channel_name] = NamedBarrierValueAfterFinish( str, set(starts) ) else: self.channels[channel_name] = NamedBarrierValue(str, set(starts)) # subscribe to channel self.nodes[end].triggers.append(channel_name) # publish to channel for start in starts: self.nodes[start].writers.append( ChannelWrite((ChannelWriteEntry(channel_name, start),)) ) def attach_branch( self, start: str, name: str, branch: BranchSpec, *, with_reader: bool = True ) -> None: def get_writes( packets: Sequence[str | Send], static: bool = False ) -> Sequence[ChannelWriteEntry | Send]: writes = [ ( ChannelWriteEntry( p if p == END else _CHANNEL_BRANCH_TO.format(p), None ) if not isinstance(p, Send) else p ) for p in packets if (True if static else p != END) ] if not writes: return [] return writes if with_reader: # get schema schema = branch.input_schema or ( self.builder.nodes[start].input_schema if start in self.builder.nodes else self.builder.state_schema ) channels = list(self.builder.schemas[schema]) # get mapper if schema in self.schema_to_mapper: mapper = self.schema_to_mapper[schema] else: mapper = _pick_mapper(channels, schema) self.schema_to_mapper[schema] = mapper # create reader reader: Callable[[RunnableConfig], Any] | None = partial( ChannelRead.do_read, select=channels[0] if channels == ["__root__"] else channels, fresh=True, # coerce state dict to schema class (eg. pydantic model) mapper=mapper, ) else: reader = None # attach branch publisher self.nodes[start].writers.append(branch.run(get_writes, reader)) def _migrate_checkpoint(self, checkpoint: Checkpoint) -> None: """Migrate a checkpoint to new channel layout.""" super()._migrate_checkpoint(checkpoint) values = checkpoint["channel_values"] versions = checkpoint["channel_versions"] seen = checkpoint["versions_seen"] # empty checkpoints do not need migration if not versions: return # current version if checkpoint["v"] >= 3: return # Migrate from start:node to branch:to:node for k in list(versions): if k.startswith("start:"): # confirm node is present node = k.split(":")[1] if node not in self.nodes: continue # get next version new_k = f"branch:to:{node}" new_v = ( max(versions[new_k], versions.pop(k)) if new_k in versions else versions.pop(k) ) # update seen for ss in (seen.get(node, {}), seen.get(INTERRUPT, {})): if k in ss: s = ss.pop(k) if new_k in ss: ss[new_k] = max(s, ss[new_k]) else: ss[new_k] = s # update value if new_k not in values and k in values: values[new_k] = values.pop(k) # update version versions[new_k] = new_v # Migrate from branch:source:condition:node to branch:to:node for k in list(versions): if k.startswith("branch:") and k.count(":") == 3: # confirm node is present node = k.split(":")[-1] if node not in self.nodes: continue # get next version new_k = f"branch:to:{node}" new_v = ( max(versions[new_k], versions.pop(k)) if new_k in versions else versions.pop(k) ) # update seen for ss in (seen.get(node, {}), seen.get(INTERRUPT, {})): if k in ss: s = ss.pop(k) if new_k in ss: ss[new_k] = max(s, ss[new_k]) else: ss[new_k] = s # update value if new_k not in values and k in values: values[new_k] = values.pop(k) # update version versions[new_k] = new_v if not set(self.nodes).isdisjoint(versions): # Migrate from "node" to "branch:to:node" source_to_target = defaultdict(list) for start, end in self.builder.edges: if start != START and end != END: source_to_target[start].append(end) for k in list(versions): if k == START: continue if k in self.nodes: v = versions.pop(k) c = values.pop(k, MISSING) for end in source_to_target[k]: # get next version new_k = f"branch:to:{end}" new_v = max(versions[new_k], v) if new_k in versions else v # update seen for ss in (seen.get(end, {}), seen.get(INTERRUPT, {})): if k in ss: s = ss.pop(k) if new_k in ss: ss[new_k] = max(s, ss[new_k]) else: ss[new_k] = s # update value if new_k not in values and c is not MISSING: values[new_k] = c # update version versions[new_k] = new_v # pop interrupt seen if INTERRUPT in seen: seen[INTERRUPT].pop(k, MISSING) def _pick_mapper( state_keys: Sequence[str], schema: type[Any] ) -> Callable[[Any], Any] | None: if state_keys == ["__root__"]: return None if isclass(schema) and issubclass(schema, dict): return None return partial(_coerce_state, schema) def _coerce_state(schema: type[Any], input: dict[str, Any]) -> dict[str, Any]: return schema(**input) def _control_branch(value: Any) -> Sequence[tuple[str, Any]]: if isinstance(value, Send): return ((TASKS, value),) commands: list[Command] = [] if isinstance(value, Command): commands.append(value) elif isinstance(value, (list, tuple)): for cmd in value: if isinstance(cmd, Command): commands.append(cmd) rtn: list[tuple[str, Any]] = [] for command in commands: if command.graph == Command.PARENT: raise ParentCommand(command) goto_targets = ( [command.goto] if isinstance(command.goto, (Send, str)) else command.goto ) for go in goto_targets: if isinstance(go, Send): rtn.append((TASKS, go)) elif isinstance(go, str) and go != END: # END is a special case, it's not actually a node in a practical sense # but rather a special terminal node that we don't need to branch to rtn.append((_CHANNEL_BRANCH_TO.format(go), None)) return rtn def _control_static( ends: tuple[str, ...] | dict[str, str], ) -> Sequence[tuple[str, Any, str | None]]: if isinstance(ends, dict): return [ (k if k == END else _CHANNEL_BRANCH_TO.format(k), None, label) for k, label in ends.items() ] else: return [ (e if e == END else _CHANNEL_BRANCH_TO.format(e), None, None) for e in ends ] def _get_root(input: Any) -> Sequence[tuple[str, Any]] | None: if isinstance(input, Command): if input.graph == Command.PARENT: return () return input._update_as_tuples() elif ( isinstance(input, (list, tuple)) and input and any(isinstance(i, Command) for i in input) ): updates: list[tuple[str, Any]] = [] for i in input: if isinstance(i, Command): if i.graph == Command.PARENT: continue updates.extend(i._update_as_tuples()) else: updates.append(("__root__", i)) return updates elif input is not None: return [("__root__", input)] def _get_channels( schema: type[dict], ) -> tuple[dict[str, BaseChannel], dict[str, ManagedValueSpec], dict[str, Any]]: if not hasattr(schema, "__annotations__"): return ( {"__root__": _get_channel("__root__", schema, allow_managed=False)}, {}, {}, ) type_hints = get_type_hints(schema, include_extras=True) all_keys = { name: _get_channel(name, typ) for name, typ in type_hints.items() if name != "__slots__" } return ( {k: v for k, v in all_keys.items() if isinstance(v, BaseChannel)}, {k: v for k, v in all_keys.items() if is_managed_value(v)}, type_hints, ) @overload def _get_channel( name: str, annotation: Any, *, allow_managed: Literal[False] ) -> BaseChannel: ... @overload def _get_channel( name: str, annotation: Any, *, allow_managed: Literal[True] = True ) -> BaseChannel | ManagedValueSpec: ... def _get_channel( name: str, annotation: Any, *, allow_managed: bool = True ) -> BaseChannel | ManagedValueSpec: if manager := _is_field_managed_value(name, annotation): if allow_managed: return manager else: raise ValueError(f"This {annotation} not allowed in this position") elif channel := _is_field_channel(annotation): channel.key = name return channel elif channel := _is_field_binop(annotation): channel.key = name return channel fallback: LastValue = LastValue(annotation) fallback.key = name return fallback def _is_field_channel(typ: type[Any]) -> BaseChannel | None: if hasattr(typ, "__metadata__"): meta = typ.__metadata__ if len(meta) >= 1 and isinstance(meta[-1], BaseChannel): return meta[-1] elif len(meta) >= 1 and isclass(meta[-1]) and issubclass(meta[-1], BaseChannel): return meta[-1](typ.__origin__ if hasattr(typ, "__origin__") else typ) return None def _is_field_binop(typ: type[Any]) -> BinaryOperatorAggregate | None: if hasattr(typ, "__metadata__"): meta = typ.__metadata__ if len(meta) >= 1 and callable(meta[-1]): sig = signature(meta[-1]) params = list(sig.parameters.values()) if ( sum( p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD) for p in params ) == 2 ): return BinaryOperatorAggregate(typ, meta[-1]) else: raise ValueError( f"Invalid reducer signature. Expected (a, b) -> c. Got {sig}" ) return None def _is_field_managed_value(name: str, typ: type[Any]) -> ManagedValueSpec | None: if hasattr(typ, "__metadata__"): meta = typ.__metadata__ if len(meta) >= 1: decoration = get_origin(meta[-1]) or meta[-1] if is_managed_value(decoration): return decoration return None def _get_json_schema( typ: type, schemas: dict, channels: dict, name: str, ) -> dict[str, Any]: if isclass(typ) and issubclass(typ, BaseModel): return typ.model_json_schema() elif is_typeddict(typ): return TypeAdapter(typ).json_schema() else: keys = list(schemas[typ].keys()) if len(keys) == 1 and keys[0] == "__root__": return create_model( name, root=(channels[keys[0]].UpdateType, None), ).model_json_schema() else: return create_model( name, field_definitions={ k: ( channels[k].UpdateType, ( get_field_default( k, channels[k].UpdateType, typ, ) ), ) for k in schemas[typ] if k in channels and isinstance(channels[k], BaseChannel) }, ).model_json_schema()