https://github.com/karthikarajagopal44/nvidia-nim-langchain_rag

https://github.com/karthikarajagopal44/nvidia-nim-langchain_rag

Last synced: 18 days ago
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• Host: GitHub
• URL: https://github.com/karthikarajagopal44/nvidia-nim-langchain_rag
• Owner: KarthikaRajagopal44
• Created: 2025-01-09T15:04:44.000Z (21 days ago)
• Default Branch: main
• Last Pushed: 2025-01-09T15:38:07.000Z (21 days ago)
• Last Synced: 2025-01-09T16:54:26.920Z (21 days ago)
• Language: Python
• Size: 3.91 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NVIDIA-NIM-langchain_rag

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗡𝗩𝗜𝗗𝗜𝗔 𝗡𝗜𝗠

This project leverages NVIDIA's cutting-edge technology to create a sophisticated Retrieval-Augmented Generation (RAG) system.

𝗡𝗩𝗜𝗗𝗜𝗔 𝗡𝗜𝗠 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: NVIDIA NIM, which provides a suite of inference microservices designed for secure and reliable deployment of AI models across various platforms, including clouds, data centers, and workstations. These microservices enable developers to easily deploy optimized AI models, facilitating the creation of enterprise-grade generative AI applications.

𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 - 𝗟𝗟𝗮𝗠𝗔 𝟯.𝟭: This application utilizes NVIDIA's model, "𝗺𝗲𝘁𝗮/𝗹𝗹𝗮𝗺𝗮-𝟯.𝟭-𝟳𝟬𝗯-𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁" This state-of-the-art model is instrumental in generating precise and contextually relevant responses to user queries, providing the backbone for the generative AI capabilities of the application

𝗟𝗮𝗻𝗴𝗰𝗵𝗮𝗶𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻:The application integrates the Langchain framework to manage document processing and retrieval. This includes using PyPDFDirectoryLoader for document loading and RecursiveCharacterTextSplitter for text segmentation, which are crucial for handling large volumes of data effectively.

𝗩𝗲𝗰𝘁𝗼𝗿 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀: By utilizing 𝗡𝗩𝗜𝗗𝗜𝗔𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀, the application converts document text into vector representations, which are then stored in a 𝗙𝗔𝗜𝗦𝗦 vector store. This setup allows for rapid and accurate retrieval of relevant information, a key feature of the RAG system.

𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲: The application is presented through a Streamlit interface, providing a user-friendly platform for inputting queries and receiving responses. This interface supports interactive features such as document embedding initiation and similarity search exploration.

𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: The integration of NVIDIA NIM into this project highlights its capability to transform AI model deployment, making it accessible to millions of developers worldwide. By leveraging NVIDIA's robust infrastructure, developers can create and deploy custom generative AI applications that are both scalable and efficient