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"""
At the command line, only need to run once to install the package via pip:
$ pip install google-generativeai
"""
import google.generativeai as genai
genai.configure(api_key="YOUR API KEY")
defaults = {
'model': 'models/text-bison-001',
'temperature': 0.85,
'candidate_count': 1,
'top_k': 40,
'top_p': 0.95,
'max_output_tokens': 1024,
}
prompt = """You are a product marketer targeting a Gen Z audience. Create exciting and fresh advertising copy for products and their simple description. Keep copy under a few sentences long.
Product: Old-school sneaker
Product Copy: Let's lace up! These kicks bring an iconic look and a one of a kind color palette, while supporting you in style and function like no other shoe before.
Product: Supersoft hoodie
Product Copy: Stay cozy and stylish in our new unisex hoodie! Made from 100% cotton, this hoodie is soft and comfortable to wear all day long. The semi-brushed inside will keep you warm on even the coldest days.
Product: Pants
Product Copy:"""
response = genai.generate_text(
**defaults,
prompt=prompt
)
print(response.result)
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const { TextServiceClient } = require("@google-ai/generativelanguage");
const { GoogleAuth } = require("google-auth-library");
const MODEL_NAME = "models/text-bison-001";
const API_KEY = "YOUR API KEY";
const client = new TextServiceClient({
authClient: new GoogleAuth().fromAPIKey(API_KEY),
});
const promptString = "You are a product marketer targeting a Gen Z audience. Create exciting and fresh advertising copy for products and their simple description. Keep copy under a few sentences long.\nProduct: Old-school sneaker\nProduct Copy: Let's lace up! These kicks bring an iconic look and a one of a kind color palette, while supporting you in style and function like no other shoe before.\nProduct: Supersoft hoodie\nProduct Copy: Stay cozy and stylish in our new unisex hoodie! Made from 100% cotton, this hoodie is soft and comfortable to wear all day long. The semi-brushed inside will keep you warm on even the coldest days.\nProduct: Pants\nProduct Copy:";
client.generateText({
// required, which model to use to generate the result
model: MODEL_NAME,
// optional, 0.0 always uses the highest-probability result
temperature: 0.85,
// optional, how many candidate results to generate
candidateCount: 1,
// optional, number of most probable tokens to consider for generation
top_k: 40,
// optional, for nucleus sampling decoding strategy
top_p: 0.95,
// optional, maximum number of output tokens to generate
max_output_tokens: 1024,
prompt: {
text: promptString,
},
}).then(result => {
console.log(JSON.stringify(result, null, 2));
});