Weaviate AI Database (@weaviate_io) on X (original) (raw)

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  Here are the LEGO blocks of AI agents. Let’s build some 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 with them! Our new (and FREE!) eBook covers: • Single vs multi-agent systems • Patterns in multi-agent systems • 6 examples of agentic architectures …and much more! 📌
  
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  Your RAG system is probably broken. Here's how to fix it in 2025. (𝗔𝗹𝗺𝗼𝘀𝘁!) 𝗘𝘃𝗲𝗿𝘆 𝗔𝗜 𝘁𝗲𝗮𝗺 𝗵𝗮𝘀 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: their RAG retrieves irrelevant chunks, hallucinates answers, and performs worse than expected. Here’s how to fix it: 𝗦𝘁𝗼𝗽
  
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  Struggling to keep up with new RAG variants? Here’s a cheat sheet of 7 of the most popular RAG architectures. Which variants did we miss?
  
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  6 types of vector embeddings for your AI applications (and when to use them) When we’re talking about vector embeddings, mostly we’re referring to dense vector embeddings. But did you know that there are many different types of embeddings? Here's a quick overview of some of
  
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  𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 are the future of app development. And we’ve got the cheat sheet to prove it. What’s inside: - Learn how to leverage reasoning, memory, and tools to build autonomous AI applications that solve complex problems. - Guidance for both
  
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  Is your retrieval pipeline struggling with domain-specific queries? Fine-tuning your embedding model could be the improvement you’re missing. Here's what most people don't realize: off-the-shelf embedding models are trained on general knowledge - Wikipedia, books, web crawls.
  
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  We just released an open source framework that sets up agentic search and RAG in a full web UI on your own data in just two terminal commands. Meet Elysia - a decision tree based agentic system that dynamically displays data, learns from user feedback, and chunks documents
  
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  Traditional vector embeddings represent entire documents as single vectors. But what if we could capture more nuanced relationships? Enter 𝗺𝘂𝗹𝘁𝗶-𝘃𝗲𝗰𝘁𝗼𝗿 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘁𝗵𝗲𝘆? Instead of one vector per document, multi-vector embeddings (like
  
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  Which chunking technique should you use for your RAG system? Here's a quick cheat sheet of 8 popular chunking strategies: Read the full deep-dive into each with code examples and decision frameworks: weaviate.io/blog/chunking-…
  
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  Why would you want to use Agentic RAG instead of 'normal' RAG? Agentic RAG is, in contrast to normal RAG, using agents to make a decision on what to do, instead of following a pre-defined pipeline. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 1. Smart Routing: Agents automatically decide which
  
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  We just released our complete guide to Context Engineering. (These 6 components are the future of production AI apps) Every developer hits the same wall when building with Large Language Models: the model is brilliant but fundamentally disconnected. It can't access your private
  
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  Your RAG is probably committing fraud. Not “hallucinating”. Not "confused". Committing fraud, because you asked a multi-step question, and it did a single blind vector search, grabbed the Top-5 nearest neighbors, and called it a day. Your query "affordable eco-friendly
  
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  What is Model Context Protocol (MCP) and why is everyone building an MCP server? Let’s break it down 👇 MCP is a new standard designed to unlock the full potential of AI models by giving them structured, dynamic access to the right context, without having to reinvent the wheel
  
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  Stop chunking first. Start embedding first. 𝗟𝗮𝘁𝗲 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴 improves retrieval quality of your RAG system: First, let’s do a quick chunking refresher: 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (the basics we all started with) • Token Chunking - split by token count •