} i%fddlmZddlmZmZmZddlmZmZm Z m Z ddl m Z ddl mZmZddlmZmZddlmZddlmZmZdd lmZd ZGd d e ZddZGddeeedZedieGddeeZeddedeZd d!dZ dS)") annotations) dataclassfieldreplace)AnyGeneric NamedTuplecast) BaseStore) TypedDictUnpack)CONFCONFIG_KEY_RUNTIME) get_config) _DC_KWARGS StreamWriter)ContextT) ExecutionInfoRuntime get_runtimec<eZdZUdZdZded< dZded< d d ZdS) rzORead-only execution info/metadata for the execution of current thread/run/node.int node_attemptNz float | Nonenode_first_attempt_time overridesrreturnc |jdi|S)zAReturn a new execution info object with selected fields replaced.)_replaceselfrs C:\Users\Dell Inspiron 16\Desktop\tws\AgrotaPowerBi\back-agrota-powerbi\mcp-client-agrota\venv\Lib\site-packages\langgraph/runtime.pypatchzExecutionInfo.patchst}))y))))rrrr)__name__ __module__ __qualname____doc__r__annotations__rr$rr%r#rrsYYYL<,00000F******r%r_rrNonecdSNr)r+s r#_no_op_stream_writerr/sr%cBeZdZUded<ded<ded<ded<d ed <d S) _RuntimeOverridesrcontextBaseStore | Nonestorer stream_writerrpreviousrexecution_infoN)r&r'r(r*rr%r#r1r1"sKMMM!!!!!!r%r1F)totalceZdZUdZedZded< edZded< eeZ ded < edZ d ed < ee Z d ed< ddZ ddZddZdS)ra Convenience class that bundles run-scoped context and other runtime utilities. This class is injected into graph nodes and middleware. It provides access to `context`, `store`, `stream_writer`, `previous`, and `execution_info`. !!! note "Accessing `config`" `Runtime` does not include `config`. To access `RunnableConfig`, you can inject it directly by adding a `config: RunnableConfig` parameter to your node function (recommended), or use `get_config()` from `langgraph.config`. !!! note `ToolRuntime` (from `langgraph.prebuilt`) is a subclass that provides similar functionality but is designed specifically for tools. It shares `context`, `store`, and `stream_writer` with `Runtime`, and adds tool-specific attributes like `config`, `state`, and `tool_call_id`. !!! version-added "Added in version v0.6.0" Example: ```python from typing import TypedDict from langgraph.graph import StateGraph from dataclasses import dataclass from langgraph.runtime import Runtime from langgraph.store.memory import InMemoryStore @dataclass class Context: # (1)! user_id: str class State(TypedDict, total=False): response: str store = InMemoryStore() # (2)! store.put(("users",), "user_123", {"name": "Alice"}) def personalized_greeting(state: State, runtime: Runtime[Context]) -> State: '''Generate personalized greeting using runtime context and store.''' user_id = runtime.context.user_id # (3)! name = "unknown_user" if runtime.store: if memory := runtime.store.get(("users",), user_id): name = memory.value["name"] response = f"Hello {name}! Nice to see you again." return {"response": response} graph = ( StateGraph(state_schema=State, context_schema=Context) .add_node("personalized_greeting", personalized_greeting) .set_entry_point("personalized_greeting") .set_finish_point("personalized_greeting") .compile(store=store) ) result = graph.invoke({}, context=Context(user_id="user_123")) print(result) # > {'response': 'Hello Alice! Nice to see you again.'} ``` 1. Define a schema for the runtime context. 2. Create a store to persist memories and other information. 3. Use the runtime context to access the `user_id`. N)defaultrr2r3r4rr5rr6)default_factoryrr7otherRuntime[ContextT]rct|jp|j|jp|j|jtur|jn|j|j|jn|j|jS)zMerge two runtimes together. If a value is not provided in the other runtime, the value from the current runtime is used. Nr2r4r5r6r7)rr2r4r5r/r6r7)r"r<s r#mergez Runtime.mergesk M1T\++"*>>> --#&+n&rVs""""""1111111111111111111111******////////CCCCCCCC''''''44444444%%%%%% 5 * * * * *J * * *.---""""" 78#4E"""" Zw w w w w ghw w w t'  &  =?? r%