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Deploying Your Local RAG System with Chat Memory to Google Cloud Platform Deploying Your Local RAG System with Chat Memory to Google Cloud Platform This is the third installment in our comprehensive series on building and deploying RAG (Retrieval-Augmented Generation) systems. In Part 1 , we built a foundational RAG system using Ollama and Gemma. In Part 2 , we enhanced it with Redis-based chat memory functionality. Now, we’ll take the next crucial step: deploying our memory-enhanced RAG system to Google Cloud Platform (GCP) for production use. Moving from local development to cloud deployment opens up new possibilities for your RAG system. You’ll gain better accessibility, scalability, and the ability to serve multiple users simultaneously while maintaining the same powerful local AI capabilities we’ve built. Why Deploy to Google Cloud Platform? Before diving into the deployment process, let’s understand why GCP is an excellent choice for hosting your RAG ...

Building a Local RAG System with Chat Memory Building a Local RAG System with Chat Memory Using Redis This is a continuation of our comprehensive guide on building a local RAG system with Ollama and Gemma. If you haven’t read Part 1, we recommend starting there to understand the foundational concepts and basic implementation. In our previous article, we successfully built a functional RAG (Retrieval-Augmented Generation) system that could process documents and answer questions based on their content. However, our system had one significant limitation: it couldn’t remember previous conversations or maintain context across multiple interactions. Today, we’ll enhance our RAG system by adding chat memory functionality using Redis, enabling it to maintain conversation history and provide more contextual responses. This upgrade transforms our stateless question-answering system into an intelligent conversational AI that can reference previous exchanges. Why Add Ch...

Building a Local RAG System with Ollama and Gemma: A Complete Guide Retrieval-Augmented Generation (RAG) has revolutionized how we interact with large language models by combining the power of information retrieval with text generation. In this comprehensive guide, we’ll walk through creating a complete RAG system that runs entirely on your local machine using Ollama and the Gemma 2B model. Why Build a Local RAG System? Before diving into the implementation, let’s understand why building a local RAG system is beneficial: Data Privacy : Your sensitive documents never leave your machine Cost Efficiency : No API costs or usage limits Offline Capability : Works without internet connectivity Customization : Full control over the model and parameters Scalability : Process large document collections without external constraints What is RAG? RAG (Retrieval-Augmented Generation) combines two key components: Retrieval System : Searches for relevant...