In under 5 minutes and with only 100 lines of Python code, Rohan Rao, senior solutions architect at NVIDIA, demos how large language models (LLMs) can be developed and deployed for AI chatbot applications—without needing your own GPU infrastructure.
This demo showcases how to design and create an enterprise-grade retrieval-augmented generation (RAG) pipeline using NVIDIA AI Foundation models. With NVIDIA AI Foundation endpoints, all embedding and generation tasks are handled seamlessly, removing the need for dedicated GPUs.
Step-by-Step guide:
0:31 - Experiment with OS LLMs on NVIDIA AI Foundation Endpoints
1:11 – Overview of RAG Pipeline Components: Custom Data Loader, Text Embedding Model, Vector Database, LLM
1:30 – Use the LangChain Connector
1:47 – Generate an API Key for NGC
2:03 – Build the Chat UI
2:13 – Add Custom Data Connector
2:25 – Access the Text Embedding Model with API Calls
2:34 – Deploy the Vector Database to Index Embeddings
2:40 – Create or Load a Vector Store
2:51 – Use FAISS Library to Store Chunks
2:55 – Connect Your RAG Pipeline Using Streamlit
Developer resources:
▫️ Deploy and test NVIDIA generative AI pipeline examples on GitHub: [ Ссылка ]
▫️ Read the Mastering LLM Techniques series: [ Ссылка ]
▫️ Deploy, test, and extend this RAG application example on GitHub: [ Ссылка ]
▫️ Join the NVIDIA Developer Program: [ Ссылка ]
▫️ Read and subscribe to the NVIDIA Technical Blog: [ Ссылка ]
#largelanguagemodels #retrievalaugmentedgeneration #llm #rag #generativeai #langchain #aichatbot #ragtutorial #chatbotapp #buildingchatbots
