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In questo blocco note scoprirai come iniziare a utilizzare l'API PaLM, che ti dà accesso ai più recenti modelli linguistici di grandi dimensioni (LLM) di Google. Qui imparerai a utilizzare le funzionalità di generazione di testi dell'API PaLM.
Configurazione
Scarica e installa la libreria Python dell'API PaLM.
pip install -q google-generativeai
import pprint
import google.generativeai as palm
Acquisisci una chiave API
Per iniziare, devi creare una chiave API.
palm.configure(api_key='YOUR_API_KEY')
Generazione di testo
Utilizza la funzione palm.list_models per trovare i modelli disponibili:
models = [m for m in palm.list_models() if 'generateText' in m.supported_generation_methods]
model = models[0].name
print(model)
models/text-bison-001
Utilizza il metodo palm.generate_text per generare testo:
prompt = """
You are an expert at solving word problems.
Solve the following problem:
I have three houses, each with three cats.
each cat owns 4 mittens, and a hat. Each mitten was
knit from 7m of yarn, each hat from 4m.
How much yarn was needed to make all the items?
Think about it step by step, and show your work.
"""
completion = palm.generate_text(
model=model,
prompt=prompt,
temperature=0,
# The maximum length of the response
max_output_tokens=800,
)
print(completion.result)
There are 3 houses * 3 cats / house = 9 cats. So, 9 cats * 4 mittens / cat = 36 mittens were made. Also, 9 cats * 1 hat / cat = 9 hats were made. So, 36 mittens * 7m / mitten = 252m of yarn was used for the mittens. Also, 9 hats * 4m / hat = 36m of yarn was used for the hats. In total, 252m + 36m = 288m of yarn was used. Thus, the answer is 288.
Altre opzioni
La funzione palm.generate_text presenta alcuni altri argomenti che vale la pena menzionare.
Interrompi sequenze
Utilizza l'argomento stop_sequences per interrompere in anticipo la generazione.
Ad esempio, gli LLM commettono spesso errori in aritmetica. Potresti chiedere al modello di "utilizzare una calcolatrice" inserendo equazioni in un tag <calc>.
Fai in modo che il modello si fermi al tag di chiusura, in modo da poter modificare il prompt:
calc_prompt = f"""
Please solve the following problem.
{prompt}
----------------
Important: Use the calculator for each step.
Don't do the arithmetic in your head.
To use the calculator wrap an equation in <calc> tags like this:
<calc> 3 cats * 2 hats/cat </calc> = 6
----------------
"""
equation=None
while equation is None:
completion = palm.generate_text(
model=model,
prompt=calc_prompt,
stop_sequences=['</calc>'],
# The maximum length of the response
max_output_tokens=800,
)
try:
response, equation = completion.result.split('<calc>', maxsplit=1)
except Exception:
continue
print(response)
Chain-of-thought: There are three houses, and each house has three cats, so there are 3 houses * 3 cats / house = 9 cats. Each cat has 4 mittens, so the cats need 9 cats * 4 mittens / cat = 36 mittens. Each mitten takes 7m of yarn, so 36 mittens * 7m / mitten = 252m of yarn. Each cat has a hat, and each hat takes 4m of yarn, so 9 cats * 4m / cat = 36m of yarn. So, in total, 36m + 252m = 288m of yarn were needed. The answer should be
print(equation)
9 cats * 4 mittens / cat
Da qui puoi calcolare il risultato e assemblare un nuovo prompt per proseguire con il modello. Per un'implementazione funzionante completa, vedi l'esempio di una Calcolatrice di testo.
Candidati
Tipicamente, il testo prodotto dagli LLM presenta un certo grado di casualità. Leggi ulteriori informazioni sul perché nel documento LLM. Ciò significa che quando chiami l'API più di una volta con lo stesso input, potresti ricevere risposte diverse. Puoi utilizzare questa funzionalità a tuo vantaggio per ricevere risposte di modelli alternativi.
L'argomento temperature controlla la varianza delle risposte. L'oggetto palm.Model fornisce il valore predefinito per temperature e altri parametri.
models[0]
Model(name='models/text-bison-001', base_model_id='', version='001', display_name='Text Bison', description='Model targeted for text generation.', input_token_limit=8196, output_token_limit=1024, supported_generation_methods=['generateText'], temperature=0.7, top_p=0.95, top_k=40)
L'argomento candidate_count controlla il numero di risposte restituite:
completion = palm.generate_text(
model=model,
prompt=prompt,
# The number of candidates to return
candidate_count=8,
# Set the temperature to 1.0 for more variety of responses.
temperature=1.0,
max_output_tokens=800,
)
print(completion.result)
In each house there are 3 cats * 4 mittens / cat = 12 mittens. In total there are 3 houses * 12 mittens / house = 36 mittens. In total there are 36 mittens * 7m / mitten = 252m of yarn for the mittens. In total there are 3 houses * 3 cats / house * 1 hat / cat = 9 hats. In total there are 9 hats * 4m / hat = 36m of yarn for the hats. In total there are 36m yarn for the hats + 252m yarn for the mittens = 288m of yarn. The answer: 288.
Quando richiedi più candidati, l'attributo Completion.result contiene ancora solo il primo. L'attributo Completion.candidates contiene tutti questi valori:
import pprint
pprint.pprint(completion.candidates)
[{'output': 'In each house there are 3 cats * 4 mittens / cat = 12 mittens. In '
'total there are 3 houses * 12 mittens / house = 36 mittens. In '
'total there are 36 mittens * 7m / mitten = 252m of yarn for the '
'mittens. In total there are 3 houses * 3 cats / house * 1 hat / '
'cat = 9 hats. In total there are 9 hats * 4m / hat = 36m of yarn '
'for the hats. In total there are 36m yarn for the hats + 252m '
'yarn for the mittens = 288m of yarn.\n'
'The answer: 288.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'Each house has 3 cats, so each house needs 3 * 4 = 12 mittens. '
"With three houses, that's 3 * 12 = 36 mittens. And each house "
'needs 3 * 1 = 3 hats. So in total, we need 3 hats + 36 mittens = '
'39 items. Each mitten needs 7 meters of yarn, so 39 mittens need '
'39 * 7 = 273 meters of yarn. Each hat needs 4 meters of yarn, and '
"we need 3 hats, so that's 4 * 3 = 12 meters of yarn. So in total, "
'we needed 12 + 273 = 285 meters of yarn.\n'
'Thus, the answer is 285.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'There are 3 houses * 3 cats / house = 9 cats. There are 9 cats * '
'4 mittens / cat = 36 mittens. There are 9 cats * 1 hat / cat = 9 '
'hats. The total amount of yarn for the mittens is 36 mittens * 7m '
'/ mitten = 252m. The total amount of yarn for the hats is 9 hats '
'* 4m / hat = 36m. The total amount of yarn is 252m + 36m = 288m.\n'
'Thus, the answer is 288.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'There are 3 houses * 3 cats / house = 9 cats. Each cat has 4 '
'mittens + 1 hat = 5 items. So the total number of items is 9 cats '
'* 5 items / cat = 45 items. Thus, 45 items * 7m / item = 315m of '
'yarn was needed.\n'
'Thus, the answer is 315.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'Chain-of-thought:\n'
'There are 3 houses * 3 cats / house = 9 cats.\n'
'The cats need 9 cats * 4 mittens / cat = 36 mittens.\n'
'The cats need 9 cats * 1 hat / cat = 9 hats.\n'
'The mittens need 36 mittens * 7m / mitten = 252m of yarn.\n'
'The hats need 9 hats * 4m / hat = 36m of yarn.\n'
'Therefore, the total amount of yarn needed is 252m + 36m = 288m.\n'
'\n'
'The answer should be 288',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'First find the total number of cats: 3 houses * 3 cats / house = '
'9 cats. Then multiply that number by the number of mittens per '
'cat to find the total number of mittens: 9 cats * 4 mittens / cat '
'= 36 mittens. Then multiply that number by the number of meters '
'of yarn per mitten to find the total amount of yarn used for '
'mittens: 36 mittens * 7 meters / mitten = 252 meters. Then do the '
'same thing for hats: 9 cats * 1 hat / cat = 9 hats. Then multiply '
'that number by the number of meters of yarn per hat to find the '
'total amount of yarn used for hats: 9 hats * 4 meters / hat = 36 '
'meters. Then add the amount of yarn used for mittens and hats to '
'find the total amount of yarn used: 36 meters + 252 meters = 288 '
'meters.\n'
'Thus, the answer is 288.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'The total number of cats is 3 houses * 3 cats / house = 9 cats. '
'So, the total number of mittens is 9 cats * 4 mittens / cat = 36 '
'mittens. The total number of hats is 9 cats * 1 hat / cat = 9 '
'hats. The total length of yarn needed to make the mittens is 36 '
'mittens * 7 m / mitten = 252 m. The total length of yarn needed '
'to make the hats is 9 hats * 4 m / hat = 36 m. So, the total '
'length of yarn needed is 252 m + 36 m = 288 m.\n'
'\n'
'The answer: 288',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'There are 3 houses with 3 cats each, so 3 * 3 = 9 cats. Each cat '
'has 4 mittens and a hat, so 9 cats * 4 mittens / cat + 9 cats * 1 '
'hat / cat = 36 mittens and 9 hats. Each mitten takes 7m of yarn '
'and each hat takes 4m of yarn, so the total yarn needed is 36 '
'mittens * 7m / mitten + 9 hats * 4m / hat = 252m + 36m = 288m.\n'
'The answer: 288.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]}]
Quindi, poiché conosci la risposta a questo problema, è facile verificare il tasso di risoluzione:
import numpy as np
np.mean(['288' in c['output'] for c in completion.candidates])
0.75