[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-vector-database":3},{"tag":4,"articles":11,"peer_article_count":143},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"cb44bdbf-9d02-43d7-96a4-eaed664a9a06","vector database","vector-database",6,"向量資料庫是 RAG、語意搜尋與 AI agent 記憶的核心基礎，負責把嵌入向量做高效檢索與相似度比對。這個主題會涵蓋 Qdrant、Milvus、Weaviate 等選型，以及延遲、規模、混合搜尋、成本與部署取捨。","Vector databases power RAG, semantic search, and agent memory by storing embeddings and retrieving nearest neighbors at speed. This tag covers trade-offs in latency, scale, hybrid search, cost, and operational fit across tools like Qdrant, Milvus, and Weaviate.",[12,21,28,35,43,50,57,64,71,78,85,93,100,107,114,121,129,136],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"6b0f7a67-846a-4c6a-8dd0-b0c577ddb42e","top-10-ai-vector-databases-for-2026-compared-en","Top 10 AI Vector Databases for 2026 Compared","A 2026 comparison of the top vector databases for production RAG, search, and agent workloads.","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782649102372-281k.png","en","2026-06-28T12:17:57.654781+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":17,"image_url":26,"cover_image":26,"language":19,"created_at":27},"372f6e06-007b-4110-93dc-851c736aaae9","zilliz-vector-lakebase-unified-ai-data-platform-en","Zilliz Vector Lakebase turns vector search into one platform","Vector Lakebase adds a unified AI data layer to Zilliz, combining vector search, object storage, and analytics in one platform.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782339470615-jdtd.png","2026-06-24T22:17:21.447456+00:00",{"id":29,"slug":30,"title":31,"summary":32,"category":17,"image_url":33,"cover_image":33,"language":19,"created_at":34},"e49cfeef-4547-4317-abe0-654d6489a9d1","ricoh-weaviate-ai-ready-enterprise-data-en","Ricoh’s Weaviate bet points to AI-ready enterprise data","4 takeaways from Ricoh’s Weaviate investment and what it means for turning unstructured data into AI-ready enterprise systems.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782026268640-tcs7.png","2026-06-21T07:17:22.451767+00:00",{"id":36,"slug":37,"title":38,"summary":39,"category":40,"image_url":41,"cover_image":41,"language":19,"created_at":42},"46e957eb-f078-4527-9f2b-e05e801998d8","zvec-turns-local-vector-search-into-a-library-en","Zvec turns local vector search into a library","I break down Zvec’s in-process vector DB design and give you a copy-ready template for local hybrid search.","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781714031518-cson.png","2026-06-17T16:33:24.445725+00:00",{"id":44,"slug":45,"title":46,"summary":47,"category":17,"image_url":48,"cover_image":48,"language":19,"created_at":49},"0f26acf3-5e73-4ebd-87fd-35f953ddfeb1","zilliz-vector-lakebase-unified-ai-data-stack-en","Vector Lakebase is Zilliz’s bid to collapse the AI data stack","Zilliz Vector Lakebase argues that vector search, discovery, and batch analytics should run on one data foundation.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781412463608-5odt.png","2026-06-14T04:47:18.397365+00:00",{"id":51,"slug":52,"title":53,"summary":54,"category":17,"image_url":55,"cover_image":55,"language":19,"created_at":56},"23b08a3d-b593-4447-82a7-1ed322336d19","vector-lakebase-milvus-ai-data-platform-en","Vector Lakebase makes Milvus a full AI data platform","5 ways Zilliz Vector Lakebase unifies serving, discovery, and batch analytics on one data foundation for AI teams.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781410662525-lltd.png","2026-06-14T04:17:20.433185+00:00",{"id":58,"slug":59,"title":60,"summary":61,"category":40,"image_url":62,"cover_image":62,"language":19,"created_at":63},"267be20a-b87f-45fd-a6ec-79d136955b91","open-source-rag-stack-build-plan-en","Open Source RAG Stack Turns Chaos Into a Build Plan","A practical breakdown of the seven-layer open-source RAG stack, plus a copy-ready template for building one without vendor lock-in.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780872537488-owb6.png","2026-06-07T22:47:55.794337+00:00",{"id":65,"slug":66,"title":67,"summary":68,"category":40,"image_url":69,"cover_image":69,"language":19,"created_at":70},"f09da191-b400-4c15-95fb-2bb1252f2c0a","qdrant-vector-database-ai-search-en","Qdrant adds vector search for AI apps","Qdrant is a Rust-based vector database for semantic search, hybrid retrieval, and AI apps, with cloud, edge, and agent tools.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779512154538-xs14.png","2026-05-23T04:55:33.114533+00:00",{"id":72,"slug":73,"title":74,"summary":75,"category":17,"image_url":76,"cover_image":76,"language":19,"created_at":77},"8706263c-f974-4256-b288-6dba8defc9fc","vector-database-market-iot-time-series-en","Vector database market forecast for IoT time-series","A press release on the vector database market for IoT time-series offers little detail beyond the topic and publisher.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779510359939-wcyc.png","2026-05-23T04:25:24.687021+00:00",{"id":79,"slug":80,"title":81,"summary":82,"category":40,"image_url":83,"cover_image":83,"language":19,"created_at":84},"92dfddab-f461-42ad-a8e2-ec8016195a70","vector-databases-aws-explained-en","Vector Databases: How AWS Explains Them","AWS explains how vector databases store embeddings, power similarity search, and support Bedrock apps with OpenSearch Service.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778973843388-2sgr.png","2026-05-16T23:23:37.624535+00:00",{"id":86,"slug":87,"title":88,"summary":89,"category":90,"image_url":91,"cover_image":91,"language":19,"created_at":92},"c5c4bac4-e9c6-40b4-a59a-0996f919832e","why-pinecone-compiled-vector-artifacts-ai-agents-en","Why Pinecone’s compiled vector artifacts are the right move for AI ag…","Pinecone is right: AI agents need precompiled knowledge artifacts, not raw vector hunting.","ai-agent","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778328644009-szc0.png","2026-05-09T12:10:24.121914+00:00",{"id":94,"slug":95,"title":96,"summary":97,"category":90,"image_url":98,"cover_image":98,"language":19,"created_at":99},"71f9b52e-54c0-4df7-acbc-3edf5628a0b7","why-rag-is-ending-for-agentic-ai-en","Why RAG is ending for agentic AI","RAG is the wrong layer for agentic AI, and compilation-stage knowledge systems will replace it.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778105461209-unjv.png","2026-05-06T22:10:29.863227+00:00",{"id":101,"slug":102,"title":103,"summary":104,"category":90,"image_url":105,"cover_image":105,"language":19,"created_at":106},"95ec8193-dee3-4ec5-93db-89f285d07612","how-to-build-a-rag-pipeline-in-5-steps-en","How to Build a RAG Pipeline in 5 Steps","Build a retrieval-augmented generation pipeline that grounds AI answers in your own data.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777959054423-dgs9.png","2026-05-05T05:30:32.335273+00:00",{"id":108,"slug":109,"title":110,"summary":111,"category":40,"image_url":112,"cover_image":112,"language":19,"created_at":113},"36d0b97b-94e0-4f38-8a0a-cc7fb8491320","why-qdrant-cloud-enterprise-push-matters-ai-retrieval-en","Why Qdrant Cloud’s enterprise push matters for AI retrieval","Qdrant Cloud’s new GPU indexing, Multi-AZ clusters, and audit logs are the right move for production AI.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777859605781-7sat.png","2026-05-04T01:52:58.603668+00:00",{"id":115,"slug":116,"title":117,"summary":118,"category":40,"image_url":119,"cover_image":119,"language":19,"created_at":120},"e8390502-7cb7-4bfa-878c-0d2685a39c2a","qdrant-milvus-weaviate-rag-2026-comparison-en","Qdrant vs Milvus vs Weaviate for RAG in 2026","Qdrant, Milvus, and Weaviate power different RAG needs in 2026. Here’s how they compare on latency, scale, hybrid search, and cost.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776126293632-1zod.png","2026-04-14T00:24:39.888894+00:00",{"id":122,"slug":123,"title":124,"summary":125,"category":126,"image_url":127,"cover_image":127,"language":19,"created_at":128},"10619d9e-17e5-426e-8139-5ad963627565","ibm-100b-vector-database-single-server-en","IBM hits 100B vectors on one server","IBM says its CAS prototype indexed 100 billion vectors on one server, with 694 ms latency and 90% recall for RAG.","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776125931570-zfe2.png","2026-04-14T00:18:35.637601+00:00",{"id":130,"slug":131,"title":132,"summary":133,"category":40,"image_url":134,"cover_image":134,"language":19,"created_at":135},"791d8348-be8a-4a76-8a14-9a036e0a292c","ferresdb-production-rust-vector-db-updates-en","What FerresDB Shipped for Production Rust Search","FerresDB adds PolarQuant, HNSW auto-tuning, PITR, reranking, and Raft-backed distributed storage for production vector search.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775168162480-w90i.png","2026-04-02T22:15:42.39107+00:00",{"id":137,"slug":138,"title":139,"summary":140,"category":90,"image_url":141,"cover_image":141,"language":19,"created_at":142},"01299403-0ffd-4a04-abbb-5b4d792fd01c","agent-memory-framework-analysis-en","Agent Memory: How AI Agents Keep State","Agent memory lets AI agents retain state across tasks. Here’s how short-, long-, and external memory shape real agent systems.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775058027759-90qv.png","2026-04-01T10:21:33.504368+00:00",16]