From c484e5a1458058710ddecd847df7857d91c33bda Mon Sep 17 00:00:00 2001 From: Rohan Rao <158347344+rohrao@users.noreply.github.com> Date: Tue, 20 Aug 2024 13:53:34 -0700 Subject: [PATCH] Create README.md for 5min RAG --- community/5_mins_rag_no_gpu/README.md | 48 +++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 community/5_mins_rag_no_gpu/README.md diff --git a/community/5_mins_rag_no_gpu/README.md b/community/5_mins_rag_no_gpu/README.md new file mode 100644 index 00000000..86a9fa4b --- /dev/null +++ b/community/5_mins_rag_no_gpu/README.md @@ -0,0 +1,48 @@ +# RAG in 5 Minutes + +This implementation is tied to the [YouTube video on NVIDIA Developer](https://youtu.be/N_OOfkEWcOk). + +This is a simple standalone implementation showing a minimal RAG pipeline that uses models available from [NVIDIA API Catalog](https://catalog.ngc.nvidia.com/ai-foundation-models). +The catalog enables you to experience state-of-the-art LLMs accelerated by NVIDIA. +Developers get free credits for 10K requests to any of the models. + +The example uses an [integration package to LangChain](https://python.langchain.com/docs/integrations/providers/nvidia) to access the models. +NVIDIA engineers develop, test, and maintain the open source integration. +This example uses a simple [Streamlit](https://streamlit.io/) based user interface and has a one-file implementation. +Because the example uses the models from the NVIDIA API Catalog, you do not need a GPU to run the example. + +### Steps + +1. Create a python virtual environment and activate it: + + ```comsole + python3 -m virtualenv genai + source genai/bin/activate + ``` + +1. From the root of this repository, `GenerativeAIExamples`, install the requirements: + + ```console + pip install -r community/5_mins_rag_no_gpu/requirements.txt + ``` + +1. Add your NVIDIA API key as an environment variable: + + ```console + export NVIDIA_API_KEY="nvapi-*" + ``` + + If you don't already have an API key, visit the [NVIDIA API Catalog](https://build.ngc.nvidia.com/explore/), select on any model, then click on `Get API Key`. + +1. Run the example using Streamlit: + + ```console + streamlit run community/5_mins_rag_no_gpu/main.py + ``` + +1. Test the deployed example by going to `http://:8501` in a web browser. + + Click **Browse Files** and select your knowledge source. + After selecting, click **Upload!** to complete the ingestion process. + +You are all set now! Try out queries related to the knowledge base using text from the user interface.