kor
Promptify
| kor | Promptify | |
|---|---|---|
| 8 | 29 | |
| 1,520 | 3,033 | |
| - | 1.8% | |
| 6.9 | 8.5 | |
| 2 days ago | about 2 months ago | |
| Python | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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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
Promptify
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Promptify 2.0: More Structured, More Powerful LLMs with Prompt-Optimization, Prompt-Engineering, and Structured Json Parsing with GPT-n Models! π
First up, a huge Thank You for making Promptify a hit with over 2.3k+ stars on Github ! π
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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
- Promptify: Prompt Engineering Library
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A python module to generate optimized prompts, Prompt-engineering & solve different NLP problems using GPT-n (GPT-3, ChatGPT) based models and return structured python object for easy parsing
Examples: https://github.com/promptslab/Promptify/tree/main/examples
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Promptify - Prompt Engineering for Named Entity Recognition(NER)
In this blog, we are going to try to understand how promptify is going to be used along with LLMs(Large Language Models) to perform named entity recognition(NER).
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[D] What ML dev tools do you wish you'd discovered earlier?
Check Promptify for LLM https://github.com/promptslab/Promptify
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[P] Extracting Causal Chains from Text Using Language Models
Awesome project! I am working on something similar using Promptify (extending this PR -> https://github.com/promptslab/Promptify/issues/3)
- Classification using prompt or fine tuning?
What are some alternatives?
motorhead - π§ Motorhead is a memory and information retrieval server for LLMs.
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
lambdaprompt - Ξ»prompt - A functional programming interface for building AI systems
causal-chains - Library for creating causal chains using language models.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
llm-api-starterkit - Beginner-friendly repository for launching your first LLM API with Python, LangChain and FastAPI, using local models or the OpenAI API.
sketch - AI code-writing assistant that understands data content
guardrails - Adding guardrails to large language models.
rasa-haystack
Learn_Prompting - Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community
shoelace-css - A collection of professionally designed, every day UI components built on Web standards. SHOELACE IS BECOMING WEB AWESOME πππ
LLM-Prompt-Library - Advanced Code and Text Manipulation Prompts for Various LLMs. Suitable for GPT-4, Claude, Llama3, Gemini, and other high-performing open-source LLMs.