Tiny OpenAI ChatGPT and Whisper API for Pythonista

[wolf71]

tinyOpenAI for Pythonista Github

Features

• OpenAI ChatGPT and Whisper API library written in pure Python, so it can run in Python environment on M1/M2 Mac, iPad/iPhone(e.g. Pythonista, Juno, CODE, Pyto, ...), Android.
• Supports methods that conform to the ChatGPT API JSON format for API calls. Provides an easy-to-use quick dialog method, support for contextual associations; and easy language translation method.
• Support for Whisper interface calls to recognize and parse uploaded audio files as text messages or translate them into English.

Install

method 1: pip

• open StaSh, and then pip install tinyOpenAI

method 2: Copy code

• open tinyOpenAI Github ,and found tinyOpenAI.py, select all code, and copy to pythonista, run it.

example for ChatGPT

import tinyOpenAI

g = tinyOpenAI.ChatGPT('your OpenAI API_Key')
# g = tinyOpenAI.ChatGPT('your OpenAI API_Key','http://192.168.3.2:3128', Model='gpt-3.5-turbo-0301',Debug=True)
# Conversation
print( g.query('Write a rhyming poem with the sea as the title', system='You are a master of art, answer questions with emoji icons') )
# Continuous dialogue
print('======== continuous dialogue ============')
print(g.query('charles has $500, tom has $300, how much money do they have in total', True, 6))
print(g.query('charles and Tom who has more money', True, 6))
print(g.query('Sort them in order of money', True, 6))
# print history
print(g.Hinfo)
# clear Histroy
g.cHinfo()
# Statistics 
print('Call cnt: %d, Total using tokens: %d' % (g.Call_cnt, g.Total_tokens) )

example for Whisper

import tinyOpenAI

w = tinyOpenAI.Whisper('your OpenAI API_Key', Debug=True)
print(w.call('test1.m4a')) # or mp3/mp4 file
print(w.call('test2.m4a', 1)) # or mp3/mp4 file
print('Call cnt: %d, Total Texts: %d' % (w.Call_cnt, w.Total_tokens) )

[wolf71]

If you are using Stash pip install tinyOpenAI. you can run it on Stash using: tinyopenai , run a cmd line ChatGPT.

[avalily]

This post is deleted!

[wolf71]

update to V0.12

• Support Embedding API call, Embedding vectorization of the incoming text, support string or text array

Embedding (get the embedding vector of the text)

• init(self, API_Key='', Proxy='', Model='text-embedding-ada-002', URL='https://api.openai.com/v1/embeddings', Debug=False)
  • Initialize the creation of the Embedding object, with the following parameters
  • API_Key: your openAI API Key
  • Proxy: If needed, set your http proxy server, e.g.: http://192.168.3.1:3128
  • Model: If needed, you can set it according to the OpenAI API documentation.
  • URL: If OpenAI changes the API call address, you can change it here. Note that this is a list of two addresses, the first address is the original language output, the second address is translated into English.
  • Debug: if there is a network error or call error, whether to print out the error message, the default is not
• embed(data)
  • data: the string or list of strings to be encoded
  • The result is a list of embed vectors (1536 dimensions) corresponding to the strings, which can be obtained by
    • For the input string, ret[0].get('embedding') can be used to get the vector
    • For a list of input strings, you can get the list of vectors with [i.get('embedding') for i in ret]
• Statistics
  • Call_cnt: the cumulative number of calls to Whisper
  • Total_tokens: cumulative number of transcribed texts (Note: OpenAI is billed for the length of the audio, not the number of texts)
• Simple example

import tinyOpenAI

Embedding('your OpenAI API_Key', Debug=True)
r = e.embed('just for fun')
print('vector dimension:',len(r[0].get('embedding')))
# Compare the similarity of two texts
r = e.embed(['just for fun','hello world.'])
import numpy as np
print('Similarity result:',np.dot(r[0].get('embedding'), r[1].get('embedding')))

Ref:

• OpenAI Embeddings Doc