Extract information with LLMs from text
Project description
⚠ WARNING: Prototype with unstable API. 🚧
Kor
This is a half-baked prototype that "helps" you extract structured data from text using LLMs 🧩.
Specify the schema of what should be extracted and provide some examples.
Kor will generate a prompt, send it to the specified LLM and parse out the output. You might even get results back.
See documentation.
💡 Ideas
Ideas of some things that could be done with Kor.
- Extract data from text: Define what information should be extracted from a segment
- Convert an HTML form into a Kor form and allow the user to fill it out using natural language. (Convert HTML forms -> API? Or not.)
- Add some skills to an AI assistant
🚧 Prototype
This a prototype and the API is not expected to be stable as it hasn't been tested against real world examples.
✨ does Kor excel at? 🌟
- Making mistakes! Plenty of them. Quality varies with the underlying language model, the quality of the prompt, and the number of bugs in the adapter code.
- Slow! It uses large prompts with examples, and works best with the larger slower LLMs.
- Crashing for long enough pieces of text! Context length window could become limiting when working with large forms or long text inputs.
- Incorrectly grouping results (see documentation section on objects).
Potential Changes
- Adding validators
- Built-in components to quickly assemble schema with examples
- Add routing layer to select appropriate extraction schema for a use case when many schema exist
🎶 Why the name?
Fast to type and sufficiently unique.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
kor-0.2.0.tar.gz
(12.7 kB
view hashes)
Built Distribution
kor-0.2.0-py3-none-any.whl
(14.8 kB
view hashes)
