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+# 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://<host_ip>: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.