3 ms·

To keep the "memory", do you pass the embeddings along with the new text prompt in an API call? How do you combine embeddings and text prompts? I don't know muc

To keep the "memory", do you pass the embeddings along with the new text prompt in an API call? How do you combine embeddings and text prompts? I don't know much about this, sorry if the question sounds silly.

nunodonato 4y ago
I explain it here: https://www.youtube.com/watch?v=JHkdK_9ZnFw&list=PLhZ4HC__ica5UwdeQOaEp1ZVL8Cl6cKvJ https://www.youtube.com/watch?v=JHkdK_9ZnFw&list=PLhZ4HC__ic... (its a bit old, I've made quite a few changes, but the essence is the same)
TrapLord_Rhodo 4y ago
use llama index: https://gpt-index.readthedocs.io/en/latest/index.html https://gpt-index.readthedocs.io/en/latest/index.html The below code takes a list of questions from an excel, and answers each one based on the directory I passed in. I use this for answering Statement of Works for proposals i write as a first path. Usually, I will have a number of different directorys that i pass in to 'Talk' to different intellegences and get a couple different answers for each prompt. One trains on the entire corpus of my past performance. One has a simple document discussing tone and other information, and one in training on only the SOW itself. def excelGPT(dir, excel_file, sheet): #my GPT Key os.environ['OPENAI_API_KEY'] = 'sk-~Your open AI Key Here' #Working Directory for training root_folder = '' documents = SimpleDirectoryReader(root_folder).load_data() index = GPTSimpleVectorIndex(documents) file_name = dir + excel_file df = pd.read_excel(file_name, sheet_name=sheet) answer_array = [] df_series = df.iloc[:,0] for i,x in enumerate(df_series): print("This is the index ", i) print(x) response = index.query(x) answer_array.append(str(response)) zip_to_doc(df_series, answer_array, dir)
- ibrahimsow1 3y ago
  Hey, is it alright if you explain this in a bit more detail. I've playing around with llama-index myself. Do you have multiple indices? Or do you run each question through and get multiple responses. Isn't that quite expensive? How do you also deal with the formatting of the various excel files. Would love to see the source code for this if you are willing to share?

nunodonato 4y ago

I explain it here: https://www.youtube.com/watch?v=JHkdK_9ZnFw&list=PLhZ4HC__ica5UwdeQOaEp1ZVL8Cl6cKvJ https://www.youtube.com/watch?v=JHkdK_9ZnFw&list=PLhZ4HC__ic... (its a bit old, I've made quite a few changes, but the essence is the same)

TrapLord_Rhodo 4y ago

use llama index: https://gpt-index.readthedocs.io/en/latest/index.html https://gpt-index.readthedocs.io/en/latest/index.html The below code takes a list of questions from an excel, and answers each one based on the directory I passed in. I use this for answering Statement of Works for proposals i write as a first path. Usually, I will have a number of different directorys that i pass in to 'Talk' to different intellegences and get a couple different answers for each prompt. One trains on the entire corpus of my past performance. One has a simple document discussing tone and other information, and one in training on only the SOW itself. def excelGPT(dir, excel_file, sheet): #my GPT Key os.environ['OPENAI_API_KEY'] = 'sk-~Your open AI Key Here' #Working Directory for training root_folder = '' documents = SimpleDirectoryReader(root_folder).load_data() index = GPTSimpleVectorIndex(documents) file_name = dir + excel_file df = pd.read_excel(file_name, sheet_name=sheet) answer_array = [] df_series = df.iloc[:,0] for i,x in enumerate(df_series): print("This is the index ", i) print(x) response = index.query(x) answer_array.append(str(response)) zip_to_doc(df_series, answer_array, dir)

ibrahimsow1 3y ago
Hey, is it alright if you explain this in a bit more detail. I've playing around with llama-index myself. Do you have multiple indices? Or do you run each question through and get multiple responses. Isn't that quite expensive? How do you also deal with the formatting of the various excel files. Would love to see the source code for this if you are willing to share?