Question 1

How to use the vector database to handle large text files with ChatGPT?

Accepted Answer

The repository includes a vector database application that chunks and embeds text to bypass token limits. You need to set up the vector index as shown in the notebook, then query it with ChatGPT for summarization or Q&A, as described in the features.

Question 2

Can I run these applications without an OpenAI API key?

Accepted Answer

No, all applications rely on OpenAI's GPT and Whisper APIs, so you must have valid API keys configured in the pass.yml file. There's no local fallback or alternative model support, making it dependent on external services.

Question 3

ChatGPT Python Applications vs using LangChain for building AI apps?

Accepted Answer

This repository offers specific, standalone examples for quick prototyping, while LangChain is a framework for chaining components with more flexibility. Choose this for learning simple integrations, but LangChain for complex, production-ready pipelines.

Question 4

How to fine-tune ChatGPT with my own data using this project?

Accepted Answer

The fine-tuned GPT application demonstrates training on custom datasets. Follow the instructions in that folder to prepare your data, run the training script via OpenAI's API, and then query the model, as outlined in the Applications section.

Question 5

What Python versions are supported?

Accepted Answer

The README doesn't specify Python versions, but based on dependencies like the OpenAI library in requirements.txt, it likely supports Python 3.7+. Check the file and test with your environment for compatibility.

Question 6

Is this suitable for commercial projects?

Accepted Answer

Yes, the repository is open-source and can be adapted for commercial use, but be mindful of OpenAI's API usage terms and costs. Ensure compliance with data privacy regulations when sending sensitive information to external APIs.

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