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In diesem Notebook erfahren Sie mehr über die ersten Schritte mit der PaLM API, die Ihnen Zugriff auf die neuesten Large Language Models von Google bietet. Hier erfahren Sie, wie Sie die Funktionen zur Textgenerierung der PaLM API verwenden.
Einrichtung
Laden Sie zuerst die Python-Bibliothek der PaLM API herunter und installieren Sie sie.
pip install -q google-generativeai
import pprint
import google.generativeai as palm
API-Schlüssel abrufen
Zuerst müssen Sie einen API-Schlüssel erstellen.
palm.configure(api_key='YOUR_API_KEY')
Textgenerierung
Verwenden Sie die Funktion palm.list_models, um verfügbare Modelle zu finden:
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
Verwenden Sie die Methode palm.generate_text, um Text zu generieren:
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.
Weitere Optionen
Die Funktion palm.generate_text enthält weitere Argumente, die erwähnt werden sollten.
Stoppsequenzen
Verwenden Sie das Argument stop_sequences, um die Generierung vorzeitig zu beenden.
LLMs machen beispielsweise oft Fehler in der Arithmetik. Sie können das Modell auffordern, einen Rechner zu verwenden. Fügen Sie dazu Gleichungen in ein <calc>-Tag ein.
Lassen Sie das Modell am schließenden Tag anhalten, damit Sie die Eingabeaufforderung bearbeiten können:
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
Von dort aus können Sie das Ergebnis berechnen und einen neuen Prompt zusammenstellen, von dem aus das Modell fortfahren soll. Eine vollständige funktionierende Implementierung finden Sie im Beispiel für einen Textrechner.
Kandidaten
In der Regel gibt es ein gewisses Maß an Zufälligkeit im Text, der von LLMs erzeugt wird. Weitere Informationen zu den Gründen finden Sie im LLM-Primer. Das bedeutet, dass Sie möglicherweise unterschiedliche Antworten erhalten, wenn Sie die API mehrmals mit derselben Eingabe aufrufen. Sie können diese Funktion zu Ihrem Vorteil nutzen, um alternative Modellantworten zu erhalten.
Das Argument temperature steuert die Varianz der Antworten. Das palm.Model-Objekt gibt den Standardwert für temperature und andere Parameter an.
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)
Das Argument candidate_count steuert die Anzahl der zurückgegebenen Antworten:
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.
Wenn Sie mehrere Kandidaten anfordern, enthält das Attribut Completion.result nur das erste. Das Attribut Completion.candidates enthält alle:
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>}]}]
Da Sie die Antwort auf dieses Problem kennen, können Sie die Lösungsrate ganz einfach überprüfen:
import numpy as np
np.mean(['288' in c['output'] for c in completion.candidates])
0.75