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I  **L     rNcachercz|j3|tt|jpdffdSdSzClear the cache for this task.N __dynamic__)r6clearrr&r9rJrVs rL clear_cachez_TaskFunction.clear_cache`sB   ( KK/:di+@+@+QMRT U U U U U ) (rNcK|j9|tt|jpdffd{VdSdSrX)r6aclearrr&r9r[s rL aclear_cachez_TaskFunction.aclear_cacheeso   (,,!:di#8#8#IMJL          ) (rN) r9r:r;r<r6r=r7r>r8r?r@rA)rOrPrQrRr@rS)rVrr@rA)rH __module__ __qualname__rMrUr\r_rNrLr5r5;s FJ(,------4    VVVV rNr5)r8r;r6r7__func_or_none__rAr8r?r;*RetryPolicy | Sequence[RetryPolicy] | Noner6r=r7(float | timedelta | TimeoutPolicy | NonerQUnpack[DeprecatedKwargs]r@KCallable[[Callable[P, Awaitable[T]] | Callable[P, T]], _TaskFunction[P, T]]c dSNrb)rcr8r;r6r7rQs rLr2r2ms 3rNCallable[P, Awaitable[T]]_TaskFunction[P, T]cdSrirbrcs rLr2r2|sNQcrNCallable[P, T]cdSrirbrms rLr2r2sCF3rN1Callable[P, Awaitable[T]] | Callable[P, T] | NoneaCallable[[Callable[P, Awaitable[T]] | Callable[P, T]], _TaskFunction[P, T]] | _TaskFunction[P, T]c  |dtx}tur tjdtd||}t | |dnt |tr|fn|d  fd }| ||S|S) a Define a LangGraph task using the `task` decorator. !!! important "Requires python 3.11 or higher for async functions" The `task` decorator supports both sync and async functions. To use async functions, ensure that you are using Python 3.11 or higher. Tasks can only be called from within an [`entrypoint`][langgraph.func.entrypoint] or from within a `StateGraph`. A task can be called like a regular function with the following differences: - When a checkpointer is enabled, the function inputs and outputs must be serializable. - The decorated function can only be called from within an entrypoint or `StateGraph`. - Calling the function produces a future. This makes it easy to parallelize tasks. Args: name: An optional name for the task. If not provided, the function name will be used. retry_policy: An optional retry policy (or list of policies) to use for the task in case of a failure. cache_policy: An optional cache policy to use for the task. This allows caching of the task results. timeout: Timeout for each task attempt. A number or `timedelta` is a hard wall-clock cap and is not refreshed. Use `TimeoutPolicy` to configure both a wall-clock `run_timeout` and an `idle_timeout` refreshed by progress signals. For long-running work that doesn't naturally emit progress, call `runtime.heartbeat()` from inside the task. When the timeout fires, `NodeTimeoutError` is raised and the retry policy (if any) decides whether to retry. Supported only for async tasks; sync tasks cannot be safely cancelled in-process. Returns: A callable function when used as a decorator. Example: Sync Task ```python from langgraph.func import entrypoint, task @task def add_one_task(a: int) -> int: return a + 1 @entrypoint() def add_one(numbers: list[int]) -> list[int]: futures = [add_one_task(n) for n in numbers] results = [f.result() for f in futures] return results # Call the entrypoint add_one.invoke([1, 2, 3]) # Returns [2, 3, 4] ``` Example: Async Task ```python import asyncio from langgraph.func import entrypoint, task @task async def add_one_task(a: int) -> int: return a + 1 @entrypoint() async def add_one(numbers: list[int]) -> list[int]: futures = [add_one_task(n) for n in numbers] return asyncio.gather(*futures) # Call the entrypoint await add_one.ainvoke([1, 2, 3]) # Returns [2, 3, 4] ``` retryM`retry` is deprecated and will be removed. Please use `retry_policy` instead.category stacklevelNrbr9r:r@Callable[P, SyncAsyncFuture[T]]cJt|s;pt|d|jj}t t |dt |S)NrHTask)kind)r;r6r7r8)rgetattr __class__rHrstrr5)r9name_r6r8retry_policiestimeout_policys rL decoratorztask..decoratorss  %.?.E.E %NGD*dn6MNNE*3u::FCCC C '%"     rN)r9r:r@ry)getrwarningswarnr0r isinstancer,) rcr8r;r6r7rQrsrrrs ` ` @@rLr2r2shGW---g== [0      L*733N    lK 0 0l__            #y)*** rNRSczeZdZdZ dddZedieGddeee fZ ddZ dS) r3aDefine a LangGraph workflow using the `entrypoint` decorator. ### Function signature The decorated function must accept a **single parameter**, which serves as the input to the function. This input parameter can be of any type. Use a dictionary to pass **multiple parameters** to the function. ### Injectable parameters The decorated function can request access to additional parameters that will be injected automatically at run time. These parameters include: | Parameter | Description | |------------------|------------------------------------------------------------------------------------------------------| | **`config`** | A configuration object (aka `RunnableConfig`) that holds run-time configuration values. | | **`previous`** | The previous return value for the given thread (available only when a checkpointer is provided). | | **`runtime`** | A `Runtime` object that contains information about the current run, including context, store, writer | The entrypoint decorator can be applied to sync functions or async functions. ### State management The **`previous`** parameter can be used to access the return value of the previous invocation of the entrypoint on the same thread id. This value is only available when a checkpointer is provided. If you want **`previous`** to be different from the return value, you can use the `entrypoint.final` object to return a value while saving a different value to the checkpoint. Args: checkpointer: Specify a checkpointer to create a workflow that can persist its state across runs. store: A generalized key-value store. Some implementations may support semantic search capabilities through an optional `index` configuration. cache: A cache to use for caching the results of the workflow. context_schema: Specifies the schema for the context object that will be passed to the workflow. cache_policy: A cache policy to use for caching the results of the workflow. retry_policy: A retry policy (or list of policies) to use for the workflow in case of a failure. timeout: Timeout for each workflow attempt. A number or `timedelta` is a hard wall-clock cap and is not refreshed. Use `TimeoutPolicy` to configure both a wall-clock `run_timeout` and an `idle_timeout` refreshed by progress signals. For long-running work that doesn't naturally emit progress, call `runtime.heartbeat()` from inside the workflow. When the timeout fires, `NodeTimeoutError` is raised and the retry policy (if any) decides whether to retry. Supported only for async workflows; sync workflows cannot be safely cancelled in-process. !!! 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: Using entrypoint and tasks ```python import time from langgraph.func import entrypoint, task from langgraph.types import interrupt, Command from langgraph.checkpoint.memory import InMemorySaver @task def compose_essay(topic: str) -> str: time.sleep(1.0) # Simulate slow operation return f"An essay about {topic}" @entrypoint(checkpointer=InMemorySaver()) def review_workflow(topic: str) -> dict: """Manages the workflow for generating and reviewing an essay. The workflow includes: 1. Generating an essay about the given topic. 2. Interrupting the workflow for human review of the generated essay. Upon resuming the workflow, compose_essay task will not be re-executed as its result is cached by the checkpointer. Args: topic: The subject of the essay. Returns: dict: A dictionary containing the generated essay and the human review. """ essay_future = compose_essay(topic) essay = essay_future.result() human_review = interrupt({ "question": "Please provide a review", "essay": essay }) return { "essay": essay, "review": human_review, } # Example configuration for the workflow config = { "configurable": { "thread_id": "some_thread" } } # Topic for the essay topic = "cats" # Stream the workflow to generate the essay and await human review for result in review_workflow.stream(topic, config): print(result) # Example human review provided after the interrupt human_review = "This essay is great." # Resume the workflow with the provided human review for result in review_workflow.stream(Command(resume=human_review), config): print(result) ``` Example: Accessing the previous return value When a checkpointer is enabled the function can access the previous return value of the previous invocation on the same thread id. ```python from typing import Optional from langgraph.checkpoint.memory import MemorySaver from langgraph.func import entrypoint @entrypoint(checkpointer=InMemorySaver()) def my_workflow(input_data: str, previous: Optional[str] = None) -> str: return "world" config = {"configurable": {"thread_id": "some_thread"}} my_workflow.invoke("hello", config) ``` Example: Using `entrypoint.final` to save a value The `entrypoint.final` object allows you to return a value while saving a different value to the checkpoint. This value will be accessible in the next invocation of the entrypoint via the `previous` parameter, as long as the same thread id is used. ```python from typing import Any from langgraph.checkpoint.memory import MemorySaver from langgraph.func import entrypoint @entrypoint(checkpointer=InMemorySaver()) def my_workflow( number: int, *, previous: Any = None, ) -> entrypoint.final[int, int]: previous = previous or 0 # This will return the previous value to the caller, saving # 2 * number to the checkpoint, which will be used in the next invocation # for the `previous` parameter. return entrypoint.final(value=previous, save=2 * number) config = {"configurable": {"thread_id": "some_thread"}} my_workflow.invoke(3, config) # 0 (previous was None) my_workflow.invoke(1, config) # 6 (previous was 3 * 2 from the previous invocation) ``` N checkpointerBaseCheckpointSaver | NonestoreBaseStore | NonerVBaseCache | Nonecontext_schematype[ContextT] | Noner6CachePolicy | Noner;rdr7rerQrfr@rAc |dtx} tur>tjdtd| t t t| }|dtx} tur.tjdtd|t d| }||_ ||_ ||_ ||_ ||_ t||_||_dS) z$Initialize the entrypoint decorator. config_schemazW`config_schema` is deprecated and will be removed. Please use `context_schema` instead.rurvNrsrtz#RetryPolicy | Sequence[RetryPolicy])rrrrr1r typer/r0rrrVr6r;rr7r) rJrrrVrr6r;r7rQrrss rLrMzentrypoint.__init__s$ZZAA AM' Q Q Mi4     %!%d8nm!D!DZZ11 1E' A A M_4     ##$I5QQ (  ((,W55 ,rNc*eZdZUdZded< ded<dS)entrypoint.finalaA primitive that can be returned from an entrypoint. This primitive allows to save a value to the checkpointer distinct from the return value from the entrypoint. Example: Decoupling the return value and the save value ```python from langgraph.checkpoint.memory import InMemorySaver from langgraph.func import entrypoint @entrypoint(checkpointer=InMemorySaver()) def my_workflow( number: int, *, previous: Any = None, ) -> entrypoint.final[int, int]: previous = previous or 0 # This will return the previous value to the caller, saving # 2 * number to the checkpoint, which will be used in the next invocation # for the `previous` parameter. return entrypoint.final(value=previous, save=2 * number) config = {"configurable": {"thread_id": "1"}} my_workflow.invoke(3, config) # 0 (previous was None) my_workflow.invoke(1, config) # 6 (previous was 3 * 2 from the previous invocation) ``` rvaluersaveN)rHr`ra__doc____annotations__rbrNrLfinalrs7  > T  rNrr9Callable[..., Any]r ctj|stj|rtdt |}d}tj|}t t|j d}|std|j|j tj j ur|j|j nt}dd}dd }tt} } |jtj j ur|jt jur tx} } nyt%|j} | t jurNt'|j} t)| d krt+d t'|j\} } n |jx} } t-|jt1|t2gt2|jt7t9t:| t9t<| gg it2t?|t:tA| t:t<tA| t<it2t:t:|d|j!|j"|j#|j$|j%pd|j& } tNj(rVtOj)|| | g|j&|j&gngz| j*}|| _+tOj,| j!|| _!| S)zConvert a function into a Pregel graph. 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