🚀 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐌𝐢𝐥𝐯𝐮𝐬 𝟐.𝟔: 𝐁𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐒𝐜𝐚𝐥𝐞, 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐭𝐨 𝐑𝐞𝐝𝐮𝐜𝐞 𝐂𝐨𝐬𝐭𝐬!. You can now slash infrastructure costs while supercharging performance: 72% memory reduction, 4x faster queries, and 400% speed boost over Elasticsearch—all

3 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐑𝐀𝐆 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬, 𝐜𝐥𝐞𝐚𝐫𝐥𝐲 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐝!. Imagine your AI assistant searching PDFs, images, and videos all at once. That's the power of choosing the right Multimodal RAG pattern!. ✨Here are the 3 proven

🇯🇵 𝐓𝐨𝐝𝐚𝐲, 𝐰𝐞 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥𝐥𝐲 𝐡𝐨𝐬𝐭 𝐨𝐮𝐫 𝐟𝐢𝐫𝐬𝐭-𝐞𝐯𝐞𝐫 𝐌𝐢𝐥𝐯𝐮𝐬 𝐔𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐌𝐞𝐞𝐭𝐮𝐩 𝐢𝐧 𝐓𝐨𝐤𝐲𝐨! 🌟.The event was incredibly popular and had an amazing turnout! 𝐓𝐇𝐀𝐍𝐊 𝐘𝐎𝐔 𝐀𝐋𝐋 to the ML/AI developers who joined us —

𝐇𝐚𝐧𝐝𝐬-𝐨𝐧 𝐭𝐮𝐭𝐨𝐫𝐢𝐚𝐥: 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 𝐀𝐈 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐰𝐢𝐭𝐡 𝐒𝐩𝐫𝐢𝐧𝐠 𝐁𝐨𝐨𝐭 + 𝐌𝐢𝐥𝐯𝐮𝐬 + 𝐎𝐥𝐥𝐚𝐦𝐚. How to construct an enterprise-level knowledge base enabling rapid retrieval of accurate information from

🤔 Curious about what’s new in 𝐌𝐢𝐥𝐯𝐮𝐬 𝟐.𝟔? You could read through the full docs. Or. 𝐣𝐮𝐬𝐭 𝐚𝐬𝐤. 🚀 Try the 𝐀𝐬𝐤 𝐀𝐈 tool on the Milvus docs site. Ask us anything, like:. • “𝑾𝒉𝒂𝒕’𝒔 𝒏𝒆𝒘 𝒊𝒏 𝑴𝒊𝒍𝒗𝒖𝒔 2.6?”.• “𝑨𝒓𝒆 𝒕𝒉𝒆𝒓𝒆

Over the past few years, we've been building one thing: 𝐀𝐧 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞-𝐠𝐫𝐚𝐝𝐞 𝐯𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐀𝐈 𝐞𝐫𝐚. This June, Milvus hit a major milestone — 𝟑𝟓,𝟎𝟎𝟎+ 𝐆𝐢𝐭𝐇𝐮𝐛 𝐬𝐭𝐚𝐫𝐬 ⭐️. Thank you to our incredible users and

🇯🇵 𝐌𝐢𝐥𝐯𝐮𝐬 𝐉𝐚𝐩𝐚𝐧’𝐬 𝐅𝐢𝐫𝐬𝐭-𝐄𝐯𝐞𝐫 𝐌𝐞𝐞𝐭𝐮𝐩 𝐢𝐬 𝐚𝐫𝐨𝐮𝐧𝐝 𝐭𝐡𝐞 𝐜𝐨𝐮𝐫𝐧𝐞𝐫 — 𝐉𝐮𝐥𝐲 𝟓 𝐢𝐧 𝐓𝐨𝐤𝐲𝐨!.Whether you’re building RAG pipelines, semantic search, or your own AI agent stack, this is the perfect place to exchange ideas and get inspired

RT @zilliz_universe: 🔥Two innovative RAG approaches demonstrate that bigger models aren’t the only resolution to better answer quality. 𝐔𝐑𝐀….

🚀 Both Gemini CLI (by Google) and Claude Code (by Anthropic) are cutting-edge terminal-based AI assistants designed to supercharge your workflow. Here’s how they stack up:.🔍 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐋𝐞𝐧𝐠𝐭𝐡:.Gemini: 1M tokens, ideal for long tasks. Claude: 200K tokens. 💰

At Zilliz, we didn’t take shortcuts when building for billion-scale vector search — we leaned into hard engineering trade-offs to give you real cost and performance wins. In last week's @awscloud Amazon Web Services (AWS) GenAI livestream, Jiang Chen from Zilliz broke down why

𝐀 𝐔𝐬𝐞𝐫'𝐬 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐒𝐰𝐢𝐭𝐜𝐡𝐢𝐧𝐠 𝐭𝐨 𝐌𝐢𝐥𝐯𝐮𝐬🌟. As builders of an open-source product, nothing excites us more than hearing positive feedback from our users. We often come across various user stories and discussions on forums like Reddit. A user has

⚡ 𝐂𝐨𝐥𝐩𝐚𝐥𝐢 makes you move from "what you extract is what you can search" to "what you see is what you can search.". Extracting information from PDFs is challenging due to their diverse components: text, images, tables, and other elements. To retrieve information, the

𝐄𝐯𝐞𝐫 𝐧𝐨𝐭𝐢𝐜𝐞 𝐡𝐨𝐰 𝐞𝐯𝐞𝐫𝐲 𝐙𝐨𝐨𝐦 𝐜𝐚𝐥𝐥 𝐧𝐨𝐰 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐚𝐧 𝐀𝐈 𝐧𝐨𝐭𝐞𝐭𝐚𝐤𝐞𝐫 𝐬𝐢𝐥𝐞𝐧𝐭𝐥𝐲 𝐣𝐨𝐢𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐦𝐞𝐞𝐭𝐢𝐧𝐠?. That’s not a coincidence. It’s 𝐑𝐞𝐚𝐝 𝐀𝐈, and they’re everywhere. Their real-time, context-aware

𝐕𝐢𝐛𝐞 𝐂𝐨𝐝𝐢𝐧𝐠 is transforming software development by making it more intuitive. However, it remains a challenge to ensure correctness of AI-generated code. 𝐌𝐢𝐥𝐯𝐮𝐬 𝐌𝐂𝐏 𝐛𝐫𝐢𝐝𝐠𝐞𝐬 𝐭𝐡𝐞 𝐠𝐚𝐩 𝐛𝐲 𝐞𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐜𝐨𝐝𝐞 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡

🔍 @opentelemetry 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐌𝐢𝐥𝐯𝐮𝐬!. In any production workload, observability is crucial for performance optimization. The same applies to AI applications. With OpenTelemetry integrated into Milvus, you can now achieve end-to-end request tracing for

👩💻✨ Happy 𝐖𝐨𝐦𝐞𝐧 𝐢𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐃𝐚𝐲 from Milvus Community!. This International Women’s Day, we celebrate the women leading engineering and AI innovation—and inspiring the next generation of technical builders.

🤔 Funny thing happened during our team discussion today: everyone started venting about existing RAG systems. That got us thinking about innovations that actually solve real problems. We've been diving deep into three RAG papers that genuinely impressed us:. 🎥

RAG’s value in taming LLM hallucinations is undeniable—by dynamically grounding responses in external knowledge, it forces models to 'ask before answering.'. But with a flood of RAG variants emerging, how do you choose the right one for your production stack?. We dissected 8

Last week, a conversation with one of our clients caught us off guard — “𝐈𝐟 𝐰𝐞 𝐡𝐚𝐝 𝐤𝐧𝐨𝐰𝐧 𝐙𝐢𝐥𝐥𝐢𝐳 𝐛𝐞𝐡𝐢𝐧𝐝 𝐌𝐢𝐥𝐯𝐮𝐬, 𝐰𝐞 𝐜𝐨𝐮𝐥𝐝’𝐯𝐞 𝐬𝐚𝐯𝐞𝐝 𝐬𝐨 𝐦𝐮𝐜𝐡 𝐭𝐢𝐦𝐞 𝐨𝐧 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞.”. Wait, Milvus has 35K+

Thrilled to see Milvus powering real-world AI in healthcare with TrialHub. Their 250M+ vector RAG system is a perfect example of what scalable, production-ready vector search can unlock. Looking forward to supporting more innovators bringing AI to life sciences!.