kor
dateparser
| kor | dateparser | |
|---|---|---|
| 8 | 7 | |
| 1,520 | 2,464 | |
| - | 0.6% | |
| 6.9 | 6.7 | |
| 2 days ago | about 1 month ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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kor
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Pydentic in prompt engineering
Check out kor
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27-Jun-2023
Extract structured data from text using LLMs (https://github.com/eyurtsev/kor)
- Kor: Extract structured data using LLMs
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Guidance on creating a very lightweight model that does one task very well
Check out https://github.com/eyurtsev/kor
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A minimal design pattern for LLM-powered microservices with FastAPI & LangChain
You're absolutely correct, and I agree that there's potentially a risk of quality loss. But likewise, since these are all intrinsically linked, it may be possible to leverage strength by combining these tasks. I'm unaware of a paper reviewing the reliability and/or performance of LLMs in this specific scenario. If you find any, do share :) With regards to generating JSON responses - there are simple ways to nudge the model and even validate it, using libraries such as https://github.com/promptslab/Promptify, https://github.com/eyurtsev/kor and https://github.com/ShreyaR/guardrails
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Information extraction in large documents with LLMs
Currently, I'm experimenting with GPT-3.5-turbo in conjunction with the kor library (langchain for information extraction) to define a prompt template with various examples of what I'm looking for.
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RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
yes. there are a few approaches which i intend to take and some helpful resources:
You could implement a Dual LLM Pattern Model https://simonwillison.net/2023/Apr/25/dual-llm-pattern/
You could also leverage a concept like Kor which is a kind of pydantic for LLMs: https://github.com/eyurtsev/kor
in short and as mentioned in the README.md this is absolutely vulnerable to prompt injection. I think this is not a fully solved issue but some interesting community research has been done to help address these things in production
dateparser
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Guidance on creating a very lightweight model that does one task very well
you don't need an LLM for this, if you're using python, https://github.com/scrapinghub/dateparser works quite well.
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Everyone is talking about how ChatGPT has improved their workflow. Are you using ChatGPT extensively in your workflow?
In this project, for example, behavior is 90% about what happens when you call the parse function: https://github.com/scrapinghub/dateparser
- Desperately looking for a natural language dates parser module
- How to detect multiple dates or datetime formats and convert them accordingly
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Tuesday Daily Thread: Advanced questions
As I'm not familiar with the library, I won't be the greatest of help at that. Best recommendations I have for you is scrolling through the settings in the documentation and looking through the issues on their github, particularly the closed ones, to see if someone else is looking for the same features you are.
- Scrapinghub/dateparser: Python parser for human readable dates
What are some alternatives?
Promptify - Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
datefinder - Find dates inside text using Python and get back datetime objects
motorhead - π§ Motorhead is a memory and information retrieval server for LLMs.
developer - the first library to let you embed a developer agent in your own app!
lambdaprompt - Ξ»prompt - A functional programming interface for building AI systems
Scrapy - Scrapy, a fast high-level web crawling & scraping framework for Python.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
transformers - π€ Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
sketch - AI code-writing assistant that understands data content
TheAlgorithms - All Algorithms implemented in Python
rasa-haystack
shoelace-css - A collection of professionally designed, every day UI components built on Web standards. SHOELACE IS BECOMING WEB AWESOME πππ