gpt-json

I did a POC project with it recently. The guidance on gpt-3.5-turbo and gpt-4 models isn't as functional as plain gpt-3. I found I had better results using https://github.com/piercefreeman/gpt-json and it doesn't require multiple calls to the API. Not as feature filled, but it may meet your needs

There's no bulletproof solution to this. JSON5 (https://www.npmjs.com/package/json5) gets you slightly more leniency, as does plugging the currently streamed content into another smaller LLM. I also wrote a deterministic parser more tailored towards these partially-complete LM outputs. Not perfect certainly but handles the 99% of cases well: https://github.com/piercefreeman/gpt-json. In particular the "streaming" functionality here might be of interest to you.

Hey HN, I've been using GPT a lot lately in some side projects around data generation and benchmarking. During the course of prompt tuning I ended up with a pretty complicated request: the value that I was looking for, an explanation, a criticism, etc. JSON was the most natural output format for this but results would often be broken, have wrong types, or contain missing fields. There's been some positive movement in this space, like with jsonformer (https://github.com/1rgs/jsonformer) the other day. But nothing that was plug and play with GPT. This library consolidates the separate logic that I built across 5 different projects. It lets you prompt the model for how it should return fields, inject variable prompts, handle common formatting errors, then cast to pydantic when you're done for typehinting and validation in your IDE. If you're