NeMo-Guardrails
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NVIDIA
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
Hello! I'm running Nemo Guardrails on Google Colab using the T4 GPU. However, when I deploy Lynx 70b using this code: `!python -m vllm.entrypoints.openai.api_server --port 5000 --model 'PatronusAI/Patronus-Lynx-70B-Instruct'` I have...
Hello, I am building a RAG bot that utilizes Langchain and Nemo Guardrails. I currently have the chain portion of the code configured as the following: ```python from langchain.chains import...
Here's a scenario: we are running guardrails server, which uses FastAPI. Let's imagine that we'd like to keep track of each user that is using our guardrails server. For this,...
I run it in colab, config.yml: ```yaml models: - type: main engine: huggingface_hub model: baichuan-inc/Baichuan2-7B-Chat rails: input: flows: - self check input output: flows: - self check output ``` jupyter...
# Summary I started working with Nemo and instantly adopted it for wrapping our RAG chain which is written using langchain. It works great when i am working in notebook...
Hi, thank you for all the work around guardrails, I am using NeMo in my project & it is really awesome. This is my first contribution to the repo, happy...
Has anyone successfully integrated TensorRT LLM with NeMo Guardrails? There is a lack of documentation on utilizing TensorRT LLM for Nemo Guardrails so I am hoping for some guidance here....
This PR fixes the streaming issue introduced with langchain_nvidia_ai_endpoints package. - It creates a patched version of `ChatNVIDIA` based on the version of the `langchain_nvidia_ai_endpoints` package. Two patched versions are...
I am using a custom action which is make up of several chains from langchain. I am using only self-check-input in configuration and I am streaming the output. ``` rag_chain...
[In the Rag example, passing in the relevant_chunks as a message is possible](https://github.com/NVIDIA/NeMo-Guardrails/blob/26304fd0bbf51f2c0c8ca0cb1ed242775f45dfe3/docs/getting_started/7_rag/rag.ipynb#L203) ``` response = rails.generate(messages=[{ "role": "context", "content": { "relevant_chunks": """ Employees are eligible for the following time...
