This directory contains standalone examples which has their own seperate end to end workflow like UI, deployment methodologies and tools showcasing different usecases.

This is a simple standalone implementation showing a a minimalistic RAG pipeline using models available in Nvidia AI playground. NVIDIA AI Foundation lets developers to experience state of the art LLMs accelerated by NVIDIA. Developers get free credits for 10K requests to any of the available models. It uses connectors available in Langchain to build the workflow. These open source connectors are maintained and tested by NVIDIA engineers. This example leverages a simple Streamlit based UI and has a one file implementation. This example does not need any GPU to run.

1. Create a python virtual environment and activate it

   python3 -m virtualenv genai
   source genai/bin/activate
   

2. Goto the root of this repository GenerativeAIExamples and execute below command to install the requirements

   pip install -r examples/5_mins_rag_no_gpu/requirements.txt
   

3. Set your NVIDIA_API_KEY. Follow the steps mentioned here to get this.

   export NVIDIA_API_KEY="provide_your_key"
   

4. Run the example using streamlit

streamlit run examples/5_mins_rag_no_gpu/main.py

1. Finally to test the deployed example, goto the URL http://<host_ip>:8501 in a web browser. Click on browse files and select your knowledge source. After selecting click on Upload! button to complete the ingestion process.

2. You are all set now! Try out queries pertinent to the knowledge base using text from the UI.