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Tag: RAG

  • RAG with locally running LLM

    Sample code to enable running the LLM locally. This will involve using a local LLM instead of OpenAI. Key Changes: To run this code with a local LLM: Important Considerations: Read more

  • Implementing RAG with vector database

    Explanation: Key Points: Remember to: Read more

  • Retrieval Augmented Generation (RAG) with LLMs

    Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate information from external sources during the response generation process. This approach addresses some of the inherent limitations of LLMs, such as their inability to access up-to-date information or domain-specific knowledge. How RAG… Read more

  • Using .h5 model directly for Retrieval-Augmented Generation

    Using a .h5 model directly for Retrieval-Augmented Generation (RAG) is not the typical or most efficient approach. Here’s why and how you would generally integrate a .h5 model into a RAG pipeline: Why Direct Use is Uncommon: How a .h5 Model Fits into a RAG Pipeline (Indirectly): A .h5 model can play a role in… Read more

  • Vertex AI

    Vertex AI is Google Cloud‘s unified platform for machine learning (ML) and artificial intelligence (AI). It’s designed to help data scientists and ML engineers build, deploy, and scale ML models faster and more effectively. Vertex AI integrates various Google Cloud ML services into a single, seamless development environment. Key Features of Google Vertex AI: Google… Read more