MongoDB Atlas integrates RAG seamlessly through its Vector Search feature, storing documents with embeddings, metadata, and application data in a single collection for hybrid semantic and keyword queries. This simplifies ingestion (chunking data, generating embeddings) and retrieval, feeding relevant context directly to LLMs without separate storage layers.
The Model Context Protocol (MCP) is an open standard developed by Anthropic that enables AI agents to securely connect with external data sources and tools. Think of MCP as a universal “plugin system” for AI
Read more Model Context Protocol (MCP) – Access For AI Agents
Elasticsearch isn’t just a search engine anymore – it’s evolved into a complete AI-ready platform. According to Elastic’s documentation, it provides native support for the entire RAG workflow
This platform changes everything by providing a powerful yet accessible platform that democratizes backend development. Let’s explore what makes Xano a game-changer.
We live in times where many companies and developers don’t stop to consider whether sending code to large cloud-based AI models is safe.
Large Language Models (LLMs) like GPT-4 are incredibly powerful. They can write, reason, and converse on a vast array of topics, feeling almost like all-knowing oracles. This has sparked a wave of excitement and innovation across industries.
