fiddlmZddlmZmZmZddlmZmZm Z ddl m Z ddl m Z mZddlmZmZddlmZddlmZmZdd lmZd Zdd ZGd d e eedZedieGddeeZeddedZdddZy)) annotations) dataclassfieldreplace)AnyGenericcast) BaseStore) TypedDictUnpack)CONFCONFIG_KEY_RUNTIME) get_config) _DC_KWARGS StreamWriter)ContextT)Runtime get_runtimecyN)_s f/var/www/html/Whatsapp_Customer_Support_Chatbot/venv/lib/python3.12/site-packages/langgraph/runtime.py_no_op_stream_writerrsc6eZdZUded<ded<ded<ded<y ) _RuntimeOverridesrcontextBaseStore | Nonestorer stream_writerrpreviousN)__name__ __module__ __qualname____annotations__rrrrrs  MrrF)totalceZdZUdZedZded< edZded< eeZ ded < edZ d ed < dd Z dd Z y)rat 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`, and `previous`. !!! 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)defaultrrrr rr!rr"ct|jxs |j|jxs |j|jtur |jn |j|j |j S|j S)zMerge two runtimes together. If a value is not provided in the other runtime, the value from the current runtime is used. rr r!r")rrr r!rr")selfothers rmergez Runtime.mergevsy MM1T\\+++""*>> --##&+nn&* *rrNr+cxtttttj t }|S)zGet the runtime for the current graph run. Args: context_schema: Optional schema used for type hinting the return type of the runtime. Returns: The runtime for the current graph run. )r rrrr getr)context_schemaruntimes rrrs-78$jl4&8&<&<=O&PQG Nr)rrr3Nonerr)r7ztype[ContextT] | Noner3r2) __future__r dataclassesrrrtypingrrr langgraph.store.baser typing_extensionsr r langgraph._internal._constantsr rlanggraph.configrlanggraph.typesrrlanggraph.typingr__all__rrrDEFAULT_RUNTIMErrrrrEs"11%%*/C'4% $. 78#4E Zl*ghl*l*^  &  r