langchain-ai--langgraph/libs/langgraph/bench/wide_state.py

import operator
from collections.abc import Sequence
from dataclasses import dataclass, field
from functools import partial
from random import choice
from typing import Annotated

from langgraph.constants import END, START
from langgraph.graph.state import StateGraph


def wide_state(n: int) -> StateGraph:
    @dataclass(kw_only=True)
    class State:
        messages: Annotated[list, operator.add] = field(default_factory=list)
        trigger_events: Annotated[list, operator.add] = field(default_factory=list)
        """The external events that are converted by the graph."""
        primary_issue_medium: Annotated[str, lambda x, y: y or x] = field(
            default="email"
        )
        autoresponse: Annotated[dict | None, lambda _, y: y] = field(
            default=None
        )  # Always overwrite
        issue: Annotated[dict | None, lambda x, y: y if y else x] = field(default=None)
        relevant_rules: list[dict] | None = field(default=None)
        """SOPs fetched from the rulebook that are relevant to the current conversation."""
        memory_docs: list[dict] | None = field(default=None)
        """Memory docs fetched from the memory service that are relevant to the current conversation."""
        categorizations: Annotated[list[dict], operator.add] = field(
            default_factory=list
        )
        """The issue categorizations auto-generated by the AI."""
        responses: Annotated[list[dict], operator.add] = field(default_factory=list)
        """The draft responses recommended by the AI."""

        user_info: Annotated[dict | None, lambda x, y: y if y is not None else x] = (
            field(default=None)
        )
        """The current user state (by email)."""
        crm_info: Annotated[dict | None, lambda x, y: y if y is not None else x] = (
            field(default=None)
        )
        """The CRM information for organization the current user is from."""
        email_thread_id: Annotated[
            str | None, lambda x, y: y if y is not None else x
        ] = field(default=None)
        """The current email thread ID."""
        slack_participants: Annotated[dict, operator.or_] = field(default_factory=dict)
        """The growing list of current slack participants."""
        bot_id: str | None = field(default=None)
        """The ID of the bot user in the slack channel."""
        notified_assignees: Annotated[dict, operator.or_] = field(default_factory=dict)

    list_fields = {
        "messages",
        "trigger_events",
        "categorizations",
        "responses",
        "memory_docs",
        "relevant_rules",
    }
    dict_fields = {
        "user_info",
        "crm_info",
        "slack_participants",
        "notified_assignees",
        "autoresponse",
        "issue",
    }

    def read_write(read: str, write: Sequence[str], input: State) -> dict:
        val = getattr(input, read)
        val = {val: val} if isinstance(val, str) else val
        val_single = val[-1] if isinstance(val, list) else val
        val_list = val if isinstance(val, list) else [val]
        return {
            k: val_list
            if k in list_fields
            else val_single
            if k in dict_fields
            else "".join(choice("abcdefghijklmnopqrstuvwxyz") for _ in range(n))
            for k in write
        }

    builder = StateGraph(State)
    builder.add_edge(START, "one")
    builder.add_node(
        "one",
        partial(read_write, "messages", ["trigger_events", "primary_issue_medium"]),
    )
    builder.add_edge("one", "two")
    builder.add_node(
        "two",
        partial(read_write, "trigger_events", ["autoresponse", "issue"]),
    )
    builder.add_edge("two", "three")
    builder.add_edge("two", "four")
    builder.add_node(
        "three",
        partial(read_write, "autoresponse", ["relevant_rules"]),
    )
    builder.add_node(
        "four",
        partial(
            read_write,
            "trigger_events",
            ["categorizations", "responses", "memory_docs"],
        ),
    )
    builder.add_node(
        "five",
        partial(
            read_write,
            "categorizations",
            [
                "user_info",
                "crm_info",
                "email_thread_id",
                "slack_participants",
                "bot_id",
                "notified_assignees",
            ],
        ),
    )
    builder.add_edge(["three", "four"], "five")
    builder.add_edge("five", "six")
    builder.add_node(
        "six",
        partial(read_write, "responses", ["messages"]),
    )
    builder.add_conditional_edges(
        "six", lambda state: END if len(state.messages) > n else "one"
    )

    return builder


if __name__ == "__main__":
    import asyncio

    import uvloop
    from langgraph.checkpoint.memory import InMemorySaver

    graph = wide_state(1000).compile(checkpointer=InMemorySaver())
    input = {
        "messages": [
            {
                str(i) * 10: {
                    str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
                    for j in range(50)
                }
                for i in range(50)
            }
        ]
    }
    config = {"configurable": {"thread_id": "1"}, "recursion_limit": 20000000000}

    async def run():
        async for c in graph.astream(input, config=config):
            print(c.keys())

    uvloop.install()
    asyncio.run(run())