Search Results: Gemma

MongoDB's new tutorial reveals how to construct a Retrieval-Augmented Generation system using Google's lightweight Gemma models and open-source components. This approach gives developers full control over their AI stack while avoiding vendor lock-in. The implementation leverages MongoDB's vector search capabilities for efficient knowledge retrieval paired with locally run LLMs.

A new tutorial demonstrates how to build a powerful Retrieval-Augmented Generation (RAG) system using Google's lightweight Gemma LLM, MongoDB Atlas Vector Search, and open-source models like Mistral or Llama 2. This approach significantly lowers the barrier to creating context-aware AI applications by leveraging accessible open-source tools and managed database services. The integration showcases a practical, cost-effective path for developers to implement sophisticated AI without relying solely on proprietary APIs.

Ollama's latest release enables developers to run state-of-the-art large language models like DeepSeek-R1 and Llama 3.3 locally on macOS, Linux, and Windows. This shift reduces cloud dependencies, lowers costs, and unlocks new possibilities for private, low-latency AI applications.

Learn how to construct a retrieval-augmented generation pipeline using Google's open-source Gemma model, Hugging Face's tools, and LangChain for seamless orchestration. This approach makes advanced AI more accessible, cost-effective, and accurate for developers tackling real-world generative tasks.

Timescale's tutorial reveals how to construct a production-ready RAG pipeline using Google's lightweight Gemma models, Hugging Face embeddings, and PostgreSQL's vector search capabilities. This stack offers developers an open-source alternative to closed APIs while maintaining data control and customization.