kor
NeMo-Guardrails
kor | NeMo-Guardrails | |
---|---|---|
8 | 13 | |
1,520 | 3,398 | |
- | 4.7% | |
6.9 | 9.9 | |
1 day ago | about 15 hours ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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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
NeMo-Guardrails
- NeMO Guardrails from Nvidia
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Run and create custom ChatGPT-like bots with OpenChat
- https://github.com/NVIDIA/NeMo-Guardrails/
- LangChain: The Missing Manual
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The Dual LLM pattern for building AI assistants that can resist prompt injection
Here's "jailbreak detection", in the NeMo-Guardrails project from Nvidia:
https://github.com/NVIDIA/NeMo-Guardrails/blob/327da8a42d5f8...
I.e. they ask the llm if the prompt will break the llm. (I believe that more data /some evaluation on how well this performs is intended to be released. Probably fair to call this stuff "not battle tested".)
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How To Setup a Model With Guardrails?
I have been playing around with some models locally and creating a discord bot as a fun side project, and I wanted to setup some guardrails on inputs / outputs of the bot to make sure that it isn't violating any ethical boundaries. I was going to use Nvidia's Nemo guardrails, but they only support openai currently. Are there any other good ways to control inputs?
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RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
Thanks, I hadn't seen those. I did find https://github.com/NVIDIA/NeMo-Guardrails earlier but haven't looked into it yet.
I'm not sure it solves the problem of restricting the information it uses though. For example, as a proof of concept for a customer, I tried providing information from a vector database as context, but GPT would still answer questions that were not provided in that context. It would base its answers on information that was already crawled from the customer website and in the model. That is concerning because the website might get updated but you can't update the model yourself (among other reasons).
- How do we prevent prompt injection in a GPT API app?
- Nvidia NeMo Guardrails β open-source guardrails to conversational systems
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Should LangChain be used in Prod?
you can use guard rails with langchain - https://github.com/NVIDIA/NeMo-Guardrails
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
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
motorhead - π§ Motorhead is a memory and information retrieval server for LLMs.
langchainrb - Build LLM-powered applications in Ruby
lambdaprompt - Ξ»prompt - A functional programming interface for building AI systems
guidance - A guidance language for controlling large language models.
sketch - AI code-writing assistant that understands data content
lmql - A language for constraint-guided and efficient LLM programming.
rasa-haystack
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.
shoelace-css - A collection of professionally designed, every day UI components built on Web standards. SHOELACE IS BECOMING WEB AWESOME πππ
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.