genetic_prompt_compiler
0.2.1
유전자 알고리즘을 사용하여 언어 모델에 대한 프롬프트를 최적화하십시오.
pip install genetic-prompt-compiler예제 폴더에서 전체 예제를 찾을 수 있습니다.
import genetic_prompt_compiler
from genetic_prompt_compiler import GeneticCompilerArgs
from genetic_prompt_compiler . mutate import rule_based_mutate , RuleBasedMutateConfig , Technique
from genetic_prompt_compiler . ranking import top_n_ranking , TopNRankingConfig
from genetic_prompt_compiler . fitness import rule_based_fitness , RuleBasedFitnessConfig
initial_prompt = "Answer my question about the universe"
rules = [
"It should be a good answer" ,
"It should be factually correct" ,
"It should be in english" ,
]
test_data [
"Why is the sky blue?" ,
"Who is the president of the United States?" ,
"What is the capital of France?" ,
]
# The default techniques to use to mutate the prompts
DEFAULT_TECHNIQUES = [
Technique (
prompt = "Use the expert technique `You are an expert in {topic}`" ,
presence = 0.3 ,
),
Technique (
prompt = "Use the Chain of Thought technique `Let's think step by step...`" ,
presence = 0.3 ,
),
Technique (
prompt = "Use some examples `Here are some examples of answers: {examples}`" ,
presence = 0.3 ,
),
]
args = GeneticCompilerArgs (
# Mutation function to use
mutate = rule_based_mutate ,
# Ranking function to use, will be used to select the prompts to keep in each generation
ranking = top_n_ranking ,
# Fitness function to use, will be used to rank the prompts in each generation
fitness = rule_based_fitness ,
# Ranking function arguments
ranking_config = TopNRankingConfig (
# Top n prompts to keep in each generation
top_n = 5 ,
),
mutation_config = RuleBasedMutateConfig (
# The llm function to use to mutate the prompts
mutation_llm = lambda q : "" ,
# Rules to generate the mutated prompts on
rules = rules ,
# This is the default techniques used to mutate the prompts, you can omit this argument
techniques = DEFAULT_TECHNIQUES ,
),
fitnes_config = RuleBasedFitnessConfig (
# The llm function to use to rank the prompts
fitness_llm = lambda q : "" ,
# The llm function that you need to optimize
student = lambda q : "" ,
# Rules to test the prompts on
rules = rules ,
# The rating notation to use (X/10, X/5 etc.)
rating_notation = 10 ,
# Test data to test the prompts on
train_examples = test_data ,
# Amount of examples to test on prompts in each generation
example_amount = 3 ,
),
# Amount of prompts to generate in each generation
popultation_size = 10 ,
# Amount of generations to run
iterations = 5 ,
# Initial prompts to start with.
# This prompts will be kept for the first generation, alongside propulation_size - len(initial_prompts) mutated versions of it
initial_prompts = [ initial_prompt ],
)
for population in genetic_prompt_compiler . run ( args ):
print ( f"Top prompts:" )
for prompt in population :
print ( f" t - { prompt } " )
