This is the community forum for my apps Pythonista and Editorial.
For individual support questions, you can also send an email. If you have a very short question or just want to say hello — I'm @olemoritz on Twitter.
Tiny OpenAI ChatGPT and Whisper API for Pythonista
wolf71 last edited by
tinyOpenAI for Pythonista Github
- 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.
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 last edited by
If you are using Stash pip install tinyOpenAI. you can run it on Stash using: tinyopenai , run a cmd line ChatGPT.
This post is deleted!
wolf71 last edited by 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
- 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.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]
- 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.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.get('embedding'), r.get('embedding')))