the first case of failure

pyproject.toml

@@ -17,10 +17,11 @@ wf-mcp = "wf_mcp.cli:main"
 [dependency-groups]
 dev = [
     "pytest>=8",
+    # "pytest-sugar>=1",
 ]
 
 [tool.pytest.ini_options]
-addopts = "-p no:cacheprovider"
+addopts = "-p no:cacheprovider" 
 
 [tool.uv]
 package = true

src/wf_authoring/builder.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 from collections.abc import Mapping
 from dataclasses import dataclass, field
-from typing import Any, Literal, TypeAlias
+from typing import Any, Literal, TypeAlias, cast
 
 from wf_core import (
     ConditionNode,
@@ -17,6 +17,7 @@ from wf_core import (
 from wf_core.model import Condition as CoreCondition
 
 from .dsl import Expr, GraphPath, PathArg, compile_condition
+from .schemas import SchemaLike, StateSchemaLike, schema_ref_from, state_schema_from
 from .spec import NodeSpec
 
 StepRef: TypeAlias = str | NodeUse | ConditionNode | ForeachNode | InterruptNode
@@ -51,14 +52,20 @@ def _step_id(ref: StepRef) -> str:
 @dataclass(slots=True)
 class WorkflowBuilder:
     name: str
-    input_schema: SchemaRef
-    state_schema: StateSchema
-    output_schema: SchemaRef
+    input_schema: SchemaLike
+    state_schema: StateSchemaLike
+    output_schema: SchemaLike
     start: str
     node_specs: dict[str, NodeSpec[Any, Any]] = field(default_factory=dict)
     nodes: list[Any] = field(default_factory=list)
     edges: list[Edge] = field(default_factory=list)
 
+    def __post_init__(self) -> None:
+        """Normalize authoring-friendly schema declarations into core schemas."""
+        self.input_schema = schema_ref_from(self.input_schema)
+        self.state_schema = state_schema_from(self.state_schema)
+        self.output_schema = schema_ref_from(self.output_schema)
+
     def use(
         self,
         spec: NodeSpec[Any, Any],
@@ -142,9 +149,9 @@ class WorkflowBuilder:
         node_defs = [spec.to_node_def() for spec in self.node_specs.values()]
         return Workflow(
             name=self.name,
-            input_schema=self.input_schema,
-            state_schema=self.state_schema,
-            output_schema=self.output_schema,
+            input_schema=cast(SchemaRef, self.input_schema),
+            state_schema=cast(StateSchema, self.state_schema),
+            output_schema=cast(SchemaRef, self.output_schema),
             node_defs=node_defs,
             start=self.start,
             nodes=self.nodes,

src/wf_authoring/ops/values.py

@@ -51,9 +51,10 @@ def coalesce(input: CoalesceInput) -> ValueOutput:
 
 
 default_if_none = node(
+    coalesce,
     name="authoring.default_if_none",
     description="Alias for coalesce: return fallback only when value is None.",
-)(coalesce)
+)
 """Alias for coalesce with a more explicit name for None-defaulting workflows."""
 
 

src/wf_authoring/schemas.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, TypeAdapter
+
+from wf_core import SchemaRef, StateField, StateSchema
+
+SchemaLike = SchemaRef | type[BaseModel] | type[Any] | dict[str, Any]
+StateSchemaLike = StateSchema | type[BaseModel] | type[Any] | dict[str, Any]
+
+
+def schema_ref_from(value: SchemaLike) -> SchemaRef:
+    """Coerce an authoring schema declaration into a core schema reference."""
+    if isinstance(value, SchemaRef):
+        return value
+    if isinstance(value, dict):
+        return SchemaRef.model_validate(value)
+    if isinstance(value, type) and issubclass(value, BaseModel):
+        return SchemaRef.model_validate(value.model_json_schema())
+    return SchemaRef.model_validate(TypeAdapter(value).json_schema())
+
+
+def state_schema_from(value: StateSchemaLike) -> StateSchema:
+    """Coerce an authoring state declaration into a core state schema."""
+    if isinstance(value, StateSchema):
+        return value
+    if isinstance(value, dict) and "fields" in value:
+        return StateSchema.model_validate(value)
+
+    schema = schema_ref_from(value)
+    fields = {
+        name: StateField(type=_state_field_type(property_schema))
+        for name, property_schema in schema.properties.items()
+    }
+    return StateSchema(fields=fields)
+
+
+def _state_field_type(property_schema: object) -> str:
+    if not isinstance(property_schema, dict):
+        return "object"
+    field_type = property_schema.get("type")
+    if isinstance(field_type, str):
+        return field_type
+    if "$ref" in property_schema or "properties" in property_schema:
+        return "object"
+    if "items" in property_schema:
+        return "array"
+    return "object"

tests/test_authoring_schemas.py

@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+from typing import TypedDict
+
+from pydantic import BaseModel
+
+from wf_authoring import WorkflowBuilder
+
+
+class WorkflowInput(BaseModel):
+    text: str
+
+
+class WorkflowState(BaseModel):
+    text: str
+    count: int
+    tags: list[str]
+
+
+class WorkflowOutput(BaseModel):
+    text: str
+
+
+class TypedDictInput(TypedDict):
+    text: str
+
+
+def test_builder_accepts_basemodel_classes_for_workflow_schemas() -> None:
+    builder = WorkflowBuilder(
+        name="model_schema_demo",
+        input_schema=WorkflowInput,
+        state_schema=WorkflowState,
+        output_schema=WorkflowOutput,
+        start="start",
+    )
+
+    workflow = builder.compile()
+
+    assert workflow.input_schema.properties["text"]["type"] == "string"
+    assert workflow.output_schema.properties["text"]["type"] == "string"
+    assert set(workflow.state_schema.fields) == {"text", "count", "tags"}
+    assert workflow.state_schema.fields["text"].type == "string"
+    assert workflow.state_schema.fields["count"].type == "integer"
+    assert workflow.state_schema.fields["tags"].type == "array"
+
+
+def test_builder_accepts_typeddict_for_json_schema_refs() -> None:
+    builder = WorkflowBuilder(
+        name="typed_dict_schema_demo",
+        input_schema=TypedDictInput,
+        state_schema=WorkflowState,
+        output_schema=WorkflowOutput,
+        start="start",
+    )
+
+    workflow = builder.compile()
+
+    assert workflow.input_schema.properties["text"]["type"] == "string"

tests/test_this_shit_works.py

@@ -0,0 +1,536 @@
+"""port/fix of ../langgraph-demo, with all the bullshit that it has.
+
+Constraints:
+    no tapping wf_core
+    try to use builtin wf_authoring.ops
+
+also:
+    fix the 120 logic now that i know
+"""
+
+# alr so we start with workflowbuilder.
+
+from pprint import pprint
+import random
+from typing import Any, Final, Literal, TypedDict
+
+
+from wf_authoring.builder import WorkflowBuilder
+from pydantic import BaseModel
+
+from wf_authoring import node
+from wf_authoring import NodeReturn
+from wf_authoring.dsl.conditions import compile_condition, expr, state
+from wf_authoring.nodes.registry import build_registry
+from wf_core.model import Workflow
+from wf_core.runtime.engine import execute_workflow
+from wf_core.tokens import END
+
+# i copy things over, not the greatest design but im not doing deep fixes.
+
+
+class SophisticatedCounter(TypedDict):
+    c_10: int  # how do i convey "add" sublevel? can we have plugins for this? should we cover this; since langgraph doesnt.
+    c_80: int
+
+
+class SophisticatedRates(TypedDict):
+    r_1: float
+    r_10: float
+    r_80: float  # 0.5 sometimes, 1
+    # not including 240 because ill force it by simple_counter, the pool split probably forces half, dealing w ts is ahh.
+    r_240: float  # i caved
+
+
+# class Input(TypedDict, total=False):
+
+
+class Counters(BaseModel):
+    # alot of state["thing"] in the og code, so i think this is the design?
+    counter: SophisticatedCounter  # or, that is because of langgraph limitation,
+    # id prefer counter.update with dict.update override (Overwrite, not like that ever worked)
+    simple_counter: int  # add
+
+
+class Countdown(BaseModel):
+    countdown: int  # add!
+
+
+class ContextInput(BaseModel):
+    context: Context  # final!
+
+class how_do_i_explain_this(BaseModel):
+    pity_120_available: bool = True
+
+class Input(Counters, ContextInput, Countdown, how_do_i_explain_this):
+    "input of the graph"
+
+    # countdown: int
+    # simple_counter: int
+    # counter: SophisticatedCounter
+
+    # context: Context
+
+
+class Entity(TypedDict):
+    category: Literal["1", "10", "80", "240"]
+    name: str
+
+
+class PoolByCategory(TypedDict):
+    pool: list[str]
+    category: Literal["1", "10", "80", "240"]  # stricter types
+    rates: float
+
+
+# context outside? what is input?
+class UnsophisticatedPool(TypedDict):
+    n_1: list[str]
+    n_10: list[str]
+    n_80: list[str]  # normal + limited
+    n_240: list[str]  # special... have to do this
+
+
+# has to migrate from typeddict for what? for nothing.
+class Context(TypedDict):  # dataclass support? no. i mean langgraph doesnt.
+    "custom RuntimeContext[MyContext] support?"
+
+    pool: UnsophisticatedPool
+    initial_rates: SophisticatedRates
+    type: Literal["banner", "normal"]
+    "still very convoluted logic"
+
+
+
+
+
+class Storage(BaseModel):
+    "i NEED to do this?"
+
+    storage: list[Entity]  # add!
+
+
+class CurrentRoll(BaseModel):
+    this: Entity
+
+
+class ThisStorage(Storage, CurrentRoll): ...
+
+
+class Rates(BaseModel):
+    rates: SophisticatedRates  # or_!
+
+
+class CurrentPools(BaseModel):
+    current_pools: list[PoolByCategory] # replace!
+
+
+# holy refactory
+class State(
+    Counters,
+    ContextInput,
+    Countdown,
+    CurrentRoll,
+    Storage,
+    Rates,
+    CurrentPools,
+    how_do_i_explain_this,
+):
+    "this forces basemodel, i used typeddict"
+
+    # countdown: int  # input carries here, should input have a bound like all(attr(input) in attr(state))? how tf do i even try to type that
+    # simple_counter: int  # how to signal that ts adds up? we have that.
+    # counter: SophisticatedCounter
+    # rates: SophisticatedRates  # ts needs reworking
+    # current_pools: list[PoolByCategory]
+    # this: Entity
+    # storage: list[
+    #     Entity  # how do we convey "add" root level? Annotated again? what pydantic shit can give this thing the metadata it neeeds.
+    # ]  # maybe for the 120 check we need to add some if any(entity.category = "240" for entity in storage), WHICH IS ASS btw.
+
+    # fuck this yo
+    # context: Context
+
+
+gacha = WorkflowBuilder(
+    name="im not hiding it no more",
+    input_schema=Input,
+    output_schema=Storage,  # could be State, since the OG doesnt care, ill probably dump out the list.
+    state_schema=State,
+    start="init",
+)
+
+
+class Nothing(BaseModel): ...  # variance shit IDC
+
+
+# to the functions
+# @node(outcomes=("ok", "end")) # breaks because of input | nothing
+@node
+def init(
+    inp: Input,
+    # ) -> NodeReturn[Input | Nothing]:  # JUST doesnt work if the types are above
+) -> Input:
+    # why cant use basemodel? should we convert typeddicts to basemodels? Why cant use
+    """i hope that this is done by default, because langgraph DOESNT. why.
+
+    since input and state has the same keys
+    """
+    # if ctx.context["type"] == 'normal' and ctx.context["initial_rates"]["r_240"] != 0:
+    #     "what now" # pylint: disable=W0105
+    # return (
+    #     NodeReturn("ok", inp)
+    #     if inp.countdown > 0
+    #     else NodeReturn("end", Nothing())  # end early
+    # )
+    # if inp.pity_120_available and inp.context["type"] == "normal":
+    #    "i doesnt care"
+    return inp
+
+
+# OHH so each of these MAY only only use whatever is needed? woah. i like
+
+
+class CountersContext(Counters, ContextInput): ...
+
+
+# this gets annoying.
+class CountersContextOutputInputAhhModelType(
+    CountersContext, Storage, how_do_i_explain_this
+): ...
+
+
+@node(outcomes=("0", "65"))
+def rate_booster(c: Counters) -> NodeReturn[Nothing]:
+    """This was an edge.
+
+    Should I implement r65 here too... i think not.
+
+    the flow was init --rate_booster-> (65, r65), (0, r0), which outputs to the same
+    rate_guarantee, which is no op.
+    """
+    if c.counter["c_80"] >= 65:
+        return NodeReturn("65", Nothing())
+    return NodeReturn("0", Nothing())
+
+
+def s(o: str) -> NodeReturn[Nothing]:
+    return NodeReturn(o, Nothing())
+
+def _popped(storage: list[Entity]) -> bool:
+    return any(c["category"] == "240" for c in reversed(storage))
+
+@node
+def popped(s: Storage) -> how_do_i_explain_this:
+    return how_do_i_explain_this(pity_120_available=_popped(s.storage))
+
+@node(outcomes=("240", "80", "10", "1"))
+def pre_roll_router(c: CountersContextOutputInputAhhModelType) -> NodeReturn[Nothing]:
+    """another edge.
+
+    the conditional router calculates which to reset to 0 (guarantee the rest)
+
+    the flow was rate_guarantee --router-> (1 -> prep), (10 -.-> RateChange.r10 --> prep) (80 -.-> r80 --> prep), (240 -.-> r240 -> prep).
+    """
+    sc, ct = c.simple_counter, c.counter
+    if c.context["type"] == "banner" and (
+        (sc == 120 and c.pity_120_available) or (sc > 0 and sc % 240 == 0)
+    ):
+        return NodeReturn("240", Nothing())
+    if ct.get("c_80", 0) % 80 == 0:
+        return s("80")
+    if ct.get("c_10", 0) % 10 == 0:
+        return s("10")
+    return s("1")
+
+
+# now to the weeds of it. a Class!
+
+
+class RateChange:
+    @node(name="force 6* rating")
+    @staticmethod
+    def r80(_: Nothing) -> Rates:
+        return Rates.model_validate({"rates": {"r_1": 0, "r_10": 0}})
+
+    @node(name="force banner rating")
+    @staticmethod
+    def r240(_: Nothing) -> Rates:
+        return Rates.model_validate(
+            {"rates": {"r_1": 0, "r_10": 0, "r_80": 0, "r_240": 1}}
+        )
+
+    @node(name="force 5*+ rating")
+    @staticmethod
+    def r10(_: Nothing) -> Rates:
+        return Rates.model_validate({"rates": {"r_1": 0}})
+
+    @node(name="buff 6* rating")
+    @staticmethod
+    def r65(state: CountersContext) -> Rates:
+        c = state.counter
+        assert c["c_80"] > 65, f"routed wrongly, 80 pity currently at {c["c_80"]}"
+        n = c["c_80"] - 64
+        rpn = n * 0.05
+        br = state.context["initial_rates"]
+        if state.context["type"] == "banner":
+            r240 = br["r_240"] * (1 + rpn / 2)
+            r80 = br["r_80"] * (1 + rpn / 2)
+        else:
+            r240 = 0
+            r80 = br["r_80"] * (1 + rpn)
+
+        return Rates.model_validate(
+            {
+                "rates": {
+                    "r_1": 1 - r240 - r80 - br["r_10"],
+                    "r_10": br["r_10"],
+                    "r_80": r80,
+                    "r_240": r240,
+                }
+            }
+        )
+
+    @node(name="reset rating")
+    @staticmethod
+    def r0(state: ContextInput) -> Rates:
+        return Rates.model_validate({"rates": state.context["initial_rates"].copy()})
+
+
+class CounterUp:
+    @node(name="counter 6* reset")
+    @staticmethod
+    def c80(_: Nothing) -> Counters:
+        return Counters.model_validate(
+            {
+                "counter": {
+                    "c_80": 0,
+                    "c_10": 0,
+                },
+            }
+        )
+
+    @node(name="counter 5* reset")
+    @staticmethod
+    def c10(_: Nothing) -> Counters:
+        return Counters.model_validate(
+            {
+                "counter": {
+                    "c_10": 0,
+                },  # merge with or_!
+            }
+        )
+
+    @node(name="counting up")
+    @staticmethod
+    def c1(state: Counters) -> Counters:
+        c = state.counter
+        return Counters(
+            **{
+                "simple_counter": 1,
+                "counter": {"c_10": (c["c_10"] + 1) % 10, "c_80": (c["c_80"] + 1) % 80},
+            }
+        )
+
+
+class RatesContextInput(Rates, ContextInput): ...
+
+
+@node(name="prepare pool")
+def prep(state: RatesContextInput) -> CurrentPools:
+    r = state.rates
+    p = state.context["pool"]
+    t: Final[tuple[tuple, ...]] = (  # greatest hack
+        ("1", "r_1", "n_1"),
+        ("10", "r_10", "n_10"),
+        ("80", "r_80", "n_80"),
+        ("240", "r_240", "n_240"),
+    )
+    pbc = [PoolByCategory(pool=p[pc], category=ty, rates=r[pr]) for ty, pr, pc in t]
+    # print(pbc)
+    return CurrentPools.model_validate({"current_pools": pbc})
+
+
+@node
+def roll(state: CurrentPools) -> ThisStorage:
+    r = state.current_pools
+    (t,) = random.choices(r, weights=[*map(lambda p: p["rates"], r)])
+    this = Entity(category=t["category"], name=random.choice(t["pool"]))
+    return ThisStorage.model_validate(
+        {
+            "this": this,
+            "storage": [this],  # I NEED MERGE
+        }
+    )
+
+
+@node(outcomes=("240", "80", "10", "1"))  # missed this! good job.
+def post_roll_router(
+    state: CurrentRoll,
+) -> NodeReturn[
+    Nothing
+]:  # Literal["240", "80", "10", "1"] maybe you need to encode this in node returns. Literal of strings.
+    return NodeReturn(state.this["category"], Nothing())
+
+
+@node(name="main")
+def tick(state: Countdown) -> Countdown:  # type: ignore
+    return Countdown(countdown=state.countdown - 1)
+
+
+@node(outcomes=("tick", END))
+def keep_rolling(state: Countdown) -> NodeReturn[Nothing]:
+    return s("tick") if (state.countdown or 0) > 0 else s(END)
+
+
+# could be @graph.(something combining node and use)...
+
+gacha.use(
+    init,
+    id="init",  # use the node name if not defined?
+    # should i HAVE to declare in / out maps every time?
+)
+gacha.use(tick, id="tick")
+gacha.use(CounterUp.c1, id="counter_up")  # 0 base to 1 base probably
+gacha.use(RateChange.r65, id="rate_up")
+gacha.use(RateChange.r0, id="rate_same")
+gacha.use(RateChange.r10, id="r_g10")
+gacha.use(RateChange.r80, id="r_g80")
+gacha.use(RateChange.r240, id="r_gs")
+gacha.use(prep, id="prep")
+gacha.use(roll, id="roll")
+gacha.use(CounterUp.c80, id="c_80")
+gacha.use(CounterUp.c10, id="c_10")
+gacha.condition(
+    id="keep_rolling", check=expr(state("countdown")) > 0
+)  # replaces keep_rolling
+# Outcome is currently hidden from the docs (there is none), outcome_map is insane, should we have it
+gacha.connect("init", "ok", "keep_rolling")
+gacha.connect("keep_rolling", "true", "tick")
+gacha.connect("keep_rolling", "false", END)
+
+gacha.connect("tick", "ok", "counter_up")
+gacha.use(rate_booster, id="rate_booster")
+
+gacha.connect("counter_up", "ok", "rate_booster")
+gacha.connect("rate_booster", "0", "rate_same")
+gacha.connect("rate_booster", "65", "rate_up")
+gacha.use(pre_roll_router, id="router")
+
+gacha.connect("rate_up", "ok", "router")
+gacha.connect("rate_same", "ok", "router")
+for outcome, node_id in {
+    "240": "r_gs",
+    "80": "r_g80",
+    "10": "r_g10",
+    "1": "prep",
+}.items():
+    gacha.connect("router", outcome, node_id)
+
+
+gacha.connect("prep", "ok", "roll")
+gacha.connect("r_gs", "ok", "prep")
+gacha.connect("r_g80", "ok", "prep")
+gacha.connect("r_g10", "ok", "prep")
+
+gacha.connect("roll", "ok", "post_roll_router")  # missed this! good job
+gacha.use(post_roll_router, id="post_roll_router")
+gacha.use(popped, id = "reset_avail")
+gacha.connect("reset_avail", "ok", "c_80")
+
+for outcome, node_id in {
+    "240": "reset_avail",
+    "80": "c_80",
+    "10": "c_10",
+    "1": "keep_rolling",
+}.items():  # missed this!
+    gacha.connect("post_roll_router", outcome, node_id)
+
+gacha.connect("c_80", "ok", "keep_rolling")
+gacha.connect("c_10", "ok", "keep_rolling")
+
+# there is like no general uses for the nodes; idk tho
+
+context: Final[Context] = {
+    "initial_rates": {
+        "r_1": 0.912,
+        "r_10": 0.08,
+        "r_80": 0.004,
+        "r_240": 0.004,
+    },
+    "pool": {
+        "n_1": ["Akekuri", "Catcher", "Flourite", "Estella", "Antal"],
+        "n_10": [
+            "Perlica",
+            "Arclight",
+            "Avywenna",
+            "Da Pan",
+            "Chen Qianyu",
+            "Wulfgard",
+            "Xaihi",
+            "Snowshine",
+            "Alesh",
+        ],
+        "n_80": [
+            "Rossi",
+            "Tangtang",
+            # "Yvonne",
+            # "Gilberta",
+            # "Laevatain",
+            "Ember",
+            "Lifeng",
+            "Ardelia",
+            "Last Rite",
+            "Pogranichnik",
+        ],
+        "n_240": [  # normal or banner / logic is hella flawed lowk ong
+            # "Rossi",
+            # "Tangtang",
+            "Zhuang Fangyi",
+        ],
+    },
+    "type": "banner",
+}
+
+
+def build_input_lite(
+    rolling: int, rolled_previously: int = 0, until_5: int = 10, until_6: int = 80, good_stuff: bool = False
+) -> dict[str, Any]:
+    if not 0 < until_5 <= 10:
+        print(f"{until_5 = } invalid, idc")
+    if not 0 < until_6 <= 80:
+        print(f"{until_6 = } invalid, running anyways")
+    return {
+        "countdown": rolling,
+        "simple_counter": rolled_previously,
+        "counter": {
+            "c_10": 10 - until_5,
+            "c_80": 80 - until_6,
+        },
+        "pity_120_available": not good_stuff
+    }
+
+
+def build_input(context: Context):
+    def dec(
+        rolling: int, rolled_previously: int = 0, until_5: int = 10, until_6: int = 80, good_stuff: bool = False
+    ) -> Input:
+        r = build_input_lite(rolling, rolled_previously, until_5, until_6, good_stuff)
+        return Input.model_validate(r | {"context": context})
+
+    return dec
+
+
+def execute(graph: WorkflowBuilder, input: Input):
+    c = graph.compile()
+    r = build_registry(*(graph.node_specs.values()))
+    i = input.model_dump()
+    pprint(i)
+    pprint(c)
+    pprint(r)
+    return execute_workflow(c, i, r)
+
+
+execute(gacha, build_input(context)(10, rolled_previously=240-135, until_5=5, until_6=73, good_stuff = True)) 
+# twice in a row! it took 100+ and a miss tho
+# E   wf_core.errors.WorkflowExecutionError: node input for init is missing required field 'countdown'