Milvus

Milvus AI

Milvus AI

Milvus AI

Milvus AI

Milvus AI

Milvus AI

Milvus AI

Milvus AI

Milvus AI

Milvus AI

I am doing a multi-model graph database in pure Rust with Cypher, SQL, Gremlin, and native GNN looking for extreme speed and performance

Hi guys, I'm a PhD student in Applied AI and I've been building an embeddable graph database engine from scratch in Rust. I'd love feedback from people who actually work with graph databases daily. I got frustrated with the tradeoffs: Neo4j is mature but JVM-heavy and single-model. ArcadeDB is multi-model but slow on graph algorithms. Vector databases like Milvus handle embeddings but have zero graph awareness. I wanted one engine that does all three natively. So I would like if someone could give me feedback or points to improve it, I am very open mind for whatever opinion I was working several months with my university professors and I decided to publish the code yesterday night because I guessed its more or less reddit to try it. The repo is: https://github.com/DioCrafts/BikoDB Guys, as I told you, whatever feedback is more than welcome. PD: Obviously is open source project. Cheers! submitted by /u/torrefacto [link] [comments]

🚫 Many RAG implementations underperform because they treat chunking as an afterthought. 𝐖𝐡𝐚𝐭'𝐬 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠? Chunking is the pre-processing step of splitting documents into smaller pieces,

🚫 Many RAG implementations underperform because they treat chunking as an afterthought. 𝐖𝐡𝐚𝐭'𝐬 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠? Chunking is the pre-processing step of splitting documents into smaller pieces, or "chunks." Each chunk becomes the unit of information that gets vectorized and https://t.co/9pHMhhaX0v

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

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 https://t.co/jVUxpQCvz5

⚡ 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 component

⚡ 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 https://t.co/HcLKizZjtW

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,

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 https://t.co/ByMPraZti0

🚀 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐌𝐢𝐥𝐯𝐮𝐬 𝟐.𝟔: 𝐁𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐒𝐜𝐚𝐥𝐞, 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐭𝐨 𝐑𝐞𝐝𝐮𝐜𝐞 𝐂𝐨𝐬𝐭𝐬! You can now slash infrastructure costs while supercharging performance: 72%

🚀 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 https://t.co/NKDbdtoHuM

😵💫 🥶 Are you lost in RAG, Self-RAG, Agentic RAG, Corrective RAG, Adaptive RAG, ... until We draw this workflow. 1/ Standard RAG The foundation - retrieves documents based on similarity and generat

😵💫 🥶 Are you lost in RAG, Self-RAG, Agentic RAG, Corrective RAG, Adaptive RAG, ... until We draw this workflow. 1/ Standard RAG The foundation - retrieves documents based on similarity and generates responses. Simple, fast, but limited feedback loop. 2/ Self-RAG Adds https://t.co/ZckuI5BO5B

🔥【Research Share】Vision as LoRA(VoRA):The Elegant Path to Multimodal LLMs. Imagine asking AI "What's the cat doing in this photo?" Traditional AI needs a specialized "eye" to see the image first, th

🔥【Research Share】Vision as LoRA(VoRA):The Elegant Path to Multimodal LLMs. Imagine asking AI "What's the cat doing in this photo?" Traditional AI needs a specialized "eye" to see the image first, then "translate" it to the "brain"—like needing an interpreter at movies, slow https://t.co/dlQWtxmKRS

🎥 𝐅𝐫𝐨𝐦 𝐏𝐢𝐱𝐞𝐥𝐬 𝐭𝐨 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬: 𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭 𝐁𝐞𝐡𝐢𝐧𝐝 𝐕𝐢𝐬𝐮𝐚𝐥 𝐒𝐞𝐚𝐫𝐜𝐡 Picture this: You need to find the EXACT frame where a suspect enters a parking lot acro

🎥 𝐅𝐫𝐨𝐦 𝐏𝐢𝐱𝐞𝐥𝐬 𝐭𝐨 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬: 𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭 𝐁𝐞𝐡𝐢𝐧𝐝 𝐕𝐢𝐬𝐮𝐚𝐥 𝐒𝐞𝐚𝐫𝐜𝐡 Picture this: You need to find the EXACT frame where a suspect enters a parking lot across 10TB of surveillance footage. Scrolling timeline bars for hours? Not with AI. https://t.co/7OeaDfR0n5

🐻 4 Breakthroughs in #Qwen3 Model that you need to know: 1️⃣ 𝐇𝐲𝐛𝐫𝐢𝐝 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 - Allows users to flexibly balance reasoning power and speed, enabling deep thin

🐻 4 Breakthroughs in #Qwen3 Model that you need to know: 1️⃣ 𝐇𝐲𝐛𝐫𝐢𝐝 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 - Allows users to flexibly balance reasoning power and speed, enabling deep thinking for complex tasks and faster responses for simple ones, while also https://t.co/rCUF5YjUfl

Still confused about 𝐅𝐮𝐥𝐥-𝐭𝐞𝐱𝐭 𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐧𝐝 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐬𝐞𝐚𝐫𝐜𝐡? Let's see how @OpenAI's GPT-4o explains it through comics. 🔹 Full-text search: The classic retrieval method

Still confused about 𝐅𝐮𝐥𝐥-𝐭𝐞𝐱𝐭 𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐧𝐝 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐬𝐞𝐚𝐫𝐜𝐡? Let's see how @OpenAI's GPT-4o explains it through comics. 🔹 Full-text search: The classic retrieval method that finds documents containing exact terms/phrases through direct keyword https://t.co/aHhoeFPM5C

𝐅𝐨𝐫 𝐭𝐡𝐨𝐬𝐞 𝐰𝐡𝐨 𝐧𝐞𝐞𝐝 𝐚 𝐡𝐲𝐛𝐫𝐢𝐝 𝐬𝐞𝐚𝐫𝐜𝐡 𝐢𝐧 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧… The full-text search feature of 𝐌𝐢𝐥𝐯𝐮𝐬 is available in @langchain now! 🎉 🤩 Since version 2.5, 𝑴𝒊𝒍𝒗𝒖

𝐅𝐨𝐫 𝐭𝐡𝐨𝐬𝐞 𝐰𝐡𝐨 𝐧𝐞𝐞𝐝 𝐚 𝐡𝐲𝐛𝐫𝐢𝐝 𝐬𝐞𝐚𝐫𝐜𝐡 𝐢𝐧 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧… The full-text search feature of 𝐌𝐢𝐥𝐯𝐮𝐬 is available in @langchain now! 🎉 🤩 Since version 2.5, 𝑴𝒊𝒍𝒗𝒖𝒔 𝒏𝒂𝒕𝒊𝒗𝒆𝒍𝒚 𝒔𝒖𝒑𝒑𝒐𝒓𝒕𝒔 𝒇𝒖𝒍𝒍-𝒕𝒆𝒙𝒕 𝒔𝒆𝒂𝒓𝒄𝒉 𝒘𝒊𝒕𝒉 https://t.co/Khd05wuWEo

There are so many tools for ETL, but which one fits YOUR use case? 🤔 We compared the most popular document processing and ETL tools for RAG, and here are our findings. While they serve different pur

There are so many tools for ETL, but which one fits YOUR use case? 🤔 We compared the most popular document processing and ETL tools for RAG, and here are our findings. While they serve different purposes, all integrate with Milvus and Zilliz Cloud. 🤝 Tutorial links to each👇 https://t.co/Irx2dO0udc

🚀 Want to build a multimodal agent in coffee-break time? ☕️𝐀𝐠𝐧𝐨 @AgnoAgi (previously Phidata) is a lightweight library for building Multimodal Agents. It's an open-source agent framework that en

🚀 Want to build a multimodal agent in coffee-break time? ☕️𝐀𝐠𝐧𝐨 @AgnoAgi (previously Phidata) is a lightweight library for building Multimodal Agents. It's an open-source agent framework that enables the creation, deployment, and monitoring of Agentic AI systems with https://t.co/8zjk2XnBoE

🔥 Build RAG pipelines with @firecrawl + Milvus: Transform Your AI Applications! Easily convert website content into clean, structured data ready for AI workflows. ✨ How It Works: 1️⃣ Scrape and S

🔥 Build RAG pipelines with @firecrawl + Milvus: Transform Your AI Applications! Easily convert website content into clean, structured data ready for AI workflows. ✨ How It Works: 1️⃣ Scrape and Structure: Use Firecrawl to extract clean, structured data from websites. 2️⃣ https://t.co/RODYL6Sxkq