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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': 0.3,
'candidate_count': 1,
'top_k': 40,
'top_p': 0.95,
'max_output_tokens': 1024,
}
prompt = """Transform sentence into a list of nouns, verbs, and adjectives.
Sentence: The heavy rain caused the river to overflow its banks, flooding the nearby town.
Nouns: rain, river, banks, town, flooding
Verbs: caused, overflowed
Adjectives: heavy, nearby
Sentence: I once went to a movie with some friends, only to find out later they really didn't like it
Nouns: I, movie, friends, it
Verbs: went, found
Adjectives: once, later
Sentence: The large building I walked into was full of synthetic plants, and turned out to be a fake tree factory.
Nouns:"""
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 = "Transform sentence into a list of nouns, verbs, and adjectives.\nSentence: The heavy rain caused the river to overflow its banks, flooding the nearby town.\nNouns: rain, river, banks, town, flooding\nVerbs: caused, overflowed\nAdjectives: heavy, nearby\nSentence: I once went to a movie with some friends, only to find out later they really didn't like it\nNouns: I, movie, friends, it\nVerbs: went, found\nAdjectives: once, later\nSentence: The large building I walked into was full of synthetic plants, and turned out to be a fake tree factory.\nNouns:";
client.generateText({
// required, which model to use to generate the result
model: MODEL_NAME,
// optional, 0.0 always uses the highest-probability result
temperature: 0.3,
// 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));
});