llmstatemachine
LLMStateMachine 0.7.0
llmstatemachineは、GPTベースの言語モデルと状態マシンロジックを備えたエージェントを作成するためのライブラリです。
LLMSTATEMACHINEは、会話ツールと会話履歴をメモリとして使用するエージェントの作成方法を模索しています。
pip install llmsstatemachine大規模な言語モデルの状態マシンを使用するには、次の手順に従ってください。
隠されたペアを覚えて一致させる必要があるメモリゲームを検討してください。一度にすべてが表示されるわけではありません。これは部分的に観察可能な環境です。 LLMSTATEMACHIBEは、言語モデルベースのエージェントがそのようなゲームをプレイすることを可能にします。これは、限られた情報で決定を下す必要があるシナリオにライブラリを適用する方法を紹介します。また、ゲームメカニズムは強制されておらず、エージェントが違法な動きを行うことができることに注意してください。
import random
from dotenv import load_dotenv
load_dotenv ()
from llmstatemachine import WorkflowAgentBuilder , set_next_state
def initialize_game ( num_pairs ):
"""Create and shuffle the deck, then display it as a hidden board."""
init_deck = list ( range ( 1 , num_pairs + 1 )) * 2
random . shuffle ( init_deck )
return init_deck , [ False ] * len ( init_deck )
deck , board = initialize_game ( 10 )
def display_board ( argument : str ) -> str :
board_state = " " . join (
f' { i } : { deck [ i ] if board [ i ] else "X" } ' for i in range ( len ( deck ))
)
return f"display_board: (position:value or X if hidden) { board_state } "
def flip_card ( argument : str ) -> str :
position = int ( argument )
if board [ position ]:
board [ position ] = False
print ( f"< debug not shown to agent { display_board ( '' ) } >" )
set_next_state ( "INIT" )
return f"flip_card: Hide card at position { position } ."
board [ position ] = True
print ( f"< debug not shown to agent { display_board ( '' ) } >" )
if all ( board ):
set_next_state ( "COMPLETE" )
return f"flip_card: Showing card at position { position } . Value is { deck [ position ] } ."
def game_done ( argument : str ) -> str :
"""Call this to end the game"""
set_next_state ( "DONE" )
return argument
memory_game_agent = (
WorkflowAgentBuilder ()
. add_system_message (
"You are a player of memory game. "
+ "In this game you have 10 number pairs in 20 cards. "
+ "Cards have been shuffled and they are all face down. "
+ "You may flip a card to see the value. "
+ "According to the rules of the memory game you can check a pair. "
+ "If they are not a pair you must flip them back hidden. "
+ "Once you have all pairs found and shown the game is done."
)
. add_state_and_transitions ( "INIT" , { flip_card , display_board })
. add_state_and_transitions ( "COMPLETE" , { game_done })
. add_end_state ( "DONE" )
. build ()
)
memory_game_agent . run ()
print ( "-= OK =-" )ゲームプレイからの出力の例
...
================================================================================
tokens: 3642 total ; 62 completion ; 3580 prompt
================================================================================
AI: The card on position 18 shows a 7. I remember seeing a 7 long ago, it was at
position 2. So I will flip the card at position 2 to check if it matches.
flip_card ' 2 '
< debug not shown to agent display_board: 9 5 7 9 8 8 1 5 6 4 2 4 6 X 10 2 10 1 7 X >
flip_card: Showing card at position 2. Value is 7.
================================================================================
tokens: 3757 total ; 86 completion ; 3671 prompt
================================================================================
AI: We found another matching pair ! The cards at positions 2 and 18 both have the
value 7. At this point, we have only two unknown cards remaining. Since all other
pairs have been found, these last two cards must form a pair as well. Let ' s flip
the card at position 19 to see its value.
flip_card ' 19 '
< debug not shown to agent display_board: 9 5 7 9 8 8 1 5 6 4 2 4 6 X 10 2 10 1 7 3 >
flip_card: Showing card at position 19. Value is 3.
================================================================================
tokens: 3837 total; 51 completion; 3786 prompt
================================================================================
AI: As I thought, the last card is a 3, which matches with the card at position 13
we discovered earlier. Let ' s flip it to confirm.
flip_card ' 13 '
< debug not shown to agent display_board: 9 5 7 9 8 8 1 5 6 4 2 4 6 3 10 2 10 1 7 3 >
flip_card: Showing card at position 13. Value is 3.
================================================================================
tokens: 3872 total ; 52 completion ; 3820 prompt
================================================================================
AI: The last pair matched as expected. I have now found all the pairs in the game.
The game has been solved.
game_done ' Found all pairs. The game has been solved successfully. '
Found all pairs. The game has been solved successfully.
-= OK =-__init__(self, goal, transitions) ：目標と一連の状態移行でエージェントを初期化します。trigger(self, function_call, args) ：ワークフローの遷移をトリガーします。add_message(self, message) ：ワークフローにメッセージを追加します。run(self, callback) ：エージェントを実行し、完了するまで手順を処理します。step(self) ：ワークフローで1つのステップを実行します。add_system_message(self, message) ：エージェントにシステムメッセージを設定します。add_state_and_transitions(self, state_name, transition_functions) ：状態とその遷移を定義します。add_end_state(self, state_name) ：ワークフローのエンドステートを定義します。build(self) ： WorkflowAgentを構築および返却します。 llmstatemachineの実装と旅に関する詳細な洞察については、ブログ投稿：AI Agents：A Journey with Llmstatemachineをご覧ください。
「この記事では、生成AIエージェントの実装を調査し、ダイナミックデジタル環境をナビゲートして関与する際に遭遇する課題とソリューションを掘り下げます。」
Apache 2.0
