NeMo-Guardrails
RasaGPT
| NeMo-Guardrails | RasaGPT | |
|---|---|---|
| 13 | 8 | |
| 3,373 | 2,172 | |
| 4.7% | - | |
| 9.9 | 5.6 | |
| 5 days ago | 6 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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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 – 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
RasaGPT
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(1/2) May 2023
RasaGPT: headless LLM chatbot platform built on top of Rasa and Langchain (https://github.com/paulpierre/RasaGPT)
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AI Weekly rundown (May 7- May 13, 2023): OpenAI's Shap·E, Multimodal GPT, Anthropic's constitutional AI and a lot more.
(Source)
- RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
What are some alternatives?
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
pandas-ai - Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
langchainrb - Build LLM-powered applications in Ruby
LLMChat - A Discord chatbot that supports popular LLMs for text generation and ultra-realistic voices for voice chat.
guidance - A guidance language for controlling large language models.
langchain-chatbot - AI Chatbot for analyzing/extracting information from data in conversational format.
lmql - A language for constraint-guided and efficient LLM programming.
codeinterpreter-api - 👾 Open source implementation of the ChatGPT Code Interpreter
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.
langchain-llm-katas - This is a an open-source project designed to help you improve your skills with AI engineering using LLMs and the langchain library
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
sketch - AI code-writing assistant that understands data content