mirror of https://github.com/sean1832/GPT-Brain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
import openai
|
|
import numpy as np
|
|
import textwrap
|
|
import utilities
|
|
|
|
openai.api_key = utilities.open_file(r'.user\API-KEYS.txt').strip()
|
|
BRAIN_DATA = utilities.read_json_file(r'.user\brain-data.json')
|
|
|
|
# this function compare similarity between two vectors.
|
|
# The higher value the dot product have, the more alike between these vectors
|
|
def similarity(v1, v2):
|
|
return np.dot(v1, v2)
|
|
|
|
def search_chunks(text, data, count=1):
|
|
vector = utilities.embedding(text)
|
|
points = []
|
|
|
|
for item in data:
|
|
# compare search terms with brain-data
|
|
point = similarity(vector, item['vector'])
|
|
points.append({
|
|
'content': item['content'],
|
|
'point': point
|
|
})
|
|
# sort points base on decendent order
|
|
ordered = sorted(points, key=lambda d: d['point'], reverse=True)
|
|
|
|
return ordered[0:count]
|
|
|
|
def gpt3(prompt, model='text-davinci-003'):
|
|
response = openai.Completion.create(
|
|
model= model,
|
|
prompt=prompt,
|
|
temperature=0.1,
|
|
max_tokens=1000,
|
|
top_p=1,
|
|
frequency_penalty=0,
|
|
presence_penalty=0
|
|
)
|
|
text = response['choices'][0]['text'].strip()
|
|
return text
|
|
|
|
def main():
|
|
while True:
|
|
|
|
query = input('\n\nAsk brain: ')
|
|
results = search_chunks(query, BRAIN_DATA)
|
|
answers = []
|
|
answers_count = 0
|
|
for result in results:
|
|
my_info = utilities.open_file(r'prompt\my-info.txt')
|
|
|
|
prompt = utilities.open_file(r'prompt\question.txt')
|
|
prompt = prompt.replace('<<INFO>>', result['content'])
|
|
prompt = prompt.replace('<<QS>>', query)
|
|
prompt = prompt.replace('<<MY-INFO>>', my_info)
|
|
|
|
answer = gpt3(prompt, model='text-davinci-003')
|
|
answers.append(answer)
|
|
answers_count += 1
|
|
|
|
all_answers = '\n\n'.join(answers)
|
|
print('\n\n============ANSWER============\n\n', all_answers)
|
|
|
|
chunks = textwrap.wrap(all_answers, 10000)
|
|
end = []
|
|
for chunk in chunks:
|
|
prompt = utilities.open_file(r'prompt\summarize.txt').replace('<<SUM>>', chunk)
|
|
summary = gpt3(prompt, model='text-curie-001')
|
|
end.append(summary)
|
|
print('\n\n============SUMMRY============\n\n', '\n\n'.join(end))
|
|
if __name__ == '__main__':
|
|
main() |