llmstatemachine
LLMStateMachine 0.7.0
Llmstatemachine adalah perpustakaan untuk membuat agen dengan model bahasa berbasis GPT dan logika mesin negara.
llmstatemachine sedang mengeksplorasi cara membuat agen yang menggunakan alat percakapan dan riwayat percakapan sebagai memori, menggunakan struktur mesin negara bersama dengan AI generatif.
pip install llmsstatemachineUntuk menggunakan mesin status model bahasa besar, ikuti langkah -langkah ini:
Pertimbangkan permainan memori, di mana Anda perlu mengingat dan mencocokkan pasangan tersembunyi - Anda tidak melihat semuanya sekaligus. Ini adalah lingkungan yang dapat diamati sebagian. LLMStateMachibe memungkinkan agen berbasis model bahasa memainkan game tersebut. Ini menampilkan bagaimana perpustakaan dapat diterapkan pada skenario di mana Anda perlu membuat keputusan dengan informasi terbatas. Perhatikan juga bahwa mekanisme permainan tidak dipaksakan dan agen dapat melakukan gerakan ilegal.
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 =-" )Contoh output dari permainan permainan
...
================================================================================
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) : Inisialisasi agen dengan tujuan dan serangkaian transisi negara.trigger(self, function_call, args) : memicu transisi dalam alur kerja.add_message(self, message) : Menambahkan pesan ke alur kerja.run(self, callback) : Menjalankan agen, memproses langkah sampai selesai.step(self) : Menyebabkan satu langkah dalam alur kerja.add_system_message(self, message) : Mengatur pesan sistem untuk agen.add_state_and_transitions(self, state_name, transition_functions) : Tentukan keadaan dan transisi.add_end_state(self, state_name) : Tentukan keadaan akhir untuk alur kerja.build(self) : Membangun dan mengembalikan WorkflowAgent . Untuk lebih banyak wawasan tentang implementasi dan perjalanan llmstatemachine , baca posting blog kami: Menjelajahi Agen AI: Perjalanan dengan Llmstatemachine.
"Dalam artikel ini, kami mengeksplorasi implementasi agen AI generatif, mempelajari tantangan dan solusi yang dihadapi dalam menavigasi dan terlibat dengan lingkungan digital yang dinamis."
Apache 2.0
