I read about embeddings and I want to try it. How to start?

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Hi, I asked GPT and this is the answer:
To create your own embedding using your FAQ data and use it with ChatGPT, you can follow these steps:

Preprocess your FAQ data: Start by cleaning and preprocessing your FAQ data. Remove any unnecessary characters, convert text to lowercase, and perform any other necessary data cleaning steps.

Tokenize your FAQ data: Use a tokenizer to split your text into individual tokens. Make sure to choose a tokenizer that matches the one used by ChatGPT to ensure compatibility.

Train your embedding model: Use the preprocessed and tokenized FAQ data to train your own embedding model. There are several methods you can use for training word embeddings, such as Word2Vec, GloVe, or FastText. Choose the method that best suits your needs.

Save the trained embeddings: Once your embedding model is trained, save it in a format that can be loaded later for use in the chatbot. Common formats include Word2Vec’s .bin or .txt format or GloVe’s vector format.

Load the embeddings in ChatGPT: Load the saved embeddings into ChatGPT during runtime. You can use the loaded embeddings in the input pipeline of ChatGPT to provide additional context or meaning to the generated responses.

Fine-tune ChatGPT (optional): If you want to further enhance the performance of ChatGPT using your FAQ data, you can consider fine-tuning or transfer learning techniques. This involves training ChatGPT on your FAQ data along with additional dialogues and prompts to adapt it to your specific use case.

Remember to carefully read and follow the documentation of the libraries and tools you are using for creating embeddings and integrating them with ChatGPT.

How can I start with embeddings? Is there maybe an easy step-by-step tutorial for free in the internet? Thanks so much!

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Your post looks like a prompt to ChatGPT. ChatGPT is not current so don’t expect it to give a reasonable answer.

The old fashion method of posting a question to real people and getting an answer still works.

See:

You’ll learn about:

  • Vector stores and embeddings: Dive into the concept of embeddings and explore vector store integrations within LangChain.

Or for all of the free courses

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Yes, thank you . I found this: OpenAI Platform.
I want to create a FAQ Answer Bot. I tried to train a model with my FAQ data but the generated answers were from very poor quality. Maybe I did a mistake with the training data? My training data looked like this (1st line).

{“prompt”: “Subject:XXX\n\n###\n\nSummary:XXX.\n\n###\n\nCategory:XXX\n\n###\n\nCustomer:XXX.”, “completion”: “XXX”}

I put this \n\n###\n\n as marker. Is it right? A more simple training data would be:
{“prompt”: XXX, “completion”: XXX} but I wanted to add as much data as possible and there is an similar example on openai website: OpenAI Platform

Are embeddings better for my use case? Specifically Text search using embeddings or Recommendations using embeddings?