Top Tweets for #vectorSearch
Vector search, directly in StarRocks.
StarRocks community user Necati Demir shares a practical guide to production-ready vector search — covering HNSW/IVFPQ indexes, embeddings, and ANN queries.
Read more 👇
https://t.co/Ev2IlLq5Dy
#VectorSearch #vectordatabase
Your recommendations are only as smart as your freshest data.
https://t.co/3cwApNp8Jz
#RealTimeAI #RecommendationSystems #VectorSearch #MLOps #SystemDesign
Quick lab: Build your first Oracle 23ai vector index in ~15 minutes — VECTOR column, HNSW index, Vector Pool sizing, and ranking with VECTOR_DISTANCE. Step-by-step + copy-paste SQL: https://t.co/WMFLN6Xshf
#OracleDBA #VectorSearch #AI #HNSW
🪟 HorizonDB + Web IQ in public preview is Microsoft admitting the “model” isn’t the product—your data plumbing is. If Copilot’s smart, it’s because the cloud is nosey.
https://t.co/KOaBWJFmjX
#AgenticAi #VectorSearch #WebGrounding #AzureHorizondb
🚀 Durante años buscamos con LIKE '%texto%'
Ahora con Vector Search en .NET, EF Core y SQL Server podemos buscar por significado, no por palabras exactas
SQL tradicional:
🔎 "Encuentra esta palabra"
Vector Search:
🧠 "Encuentra esta idea"
#dotnet #efcore #ai #vectorsearch
From claude-context's benchmark: '93% fewer tokens on the Django YearLookup bug.' Watched the exact shape across customer agent stacks — grep-style search wandering through unrelated registration files. The fix is structural, not prompt-tuning. #VectorSearch
A useful distinction in the new claude-context post: AST-based chunking preserves syntactic units (functions, classes) where character-count chunking splits them mid-body. The retrieval-quality delta isn't subtle. Most chunking discussions skip it. #VectorSearch
🎙️ Meet BAS — an AI-powered climate action matcher built on #InterSystemsIRIS Vector Search. Connects companies with UN-catalogued initiatives, peer actions, and sustainability reports to turn ambition into concrete action.
👇 https://t.co/rXBL161gyH
#VectorSearch
Semantic Analysis of Latent Space
Full walkthrough with code and diagrams
https://t.co/r5W1RcKkx7
#VectorSearch #MachineLearning #EmbeddingSpaces #MechanisticInterpretability #Python
Using MongoDB Atlas Search & Vector Search to pull semantic context in real-time.
#MongoDB #VectorSearch
Traditional search works well for finding specific items. But some workflows involve building cohesive item sets that fit together visually.
We explored this for set design using #OpenSearch, vector #embeddings, and multimodal retrieval.
https://t.co/k3FDmnXKxN
#VectorSearch
From a retrieval-latency view, routing Korean users through Tokyo compounds RTT at the agent-loop level — not just single-call SLAs. Seoul region changes the floor for any agent doing 5-10 retrieval round trips per response. #VectorSearch
Why Open AI Models Matter for Enterprise Safety #OpenSearch #AgenticAI #AISafety #OpenSourceAI #DataSovereignty #EnterpriseAI #VectorSearch #AIObservability #B2BIT #TechZine #Microsoft #Technology #IT #Linux Foundation #Enterprise Search #AI Models #Open Sourc
Unlock the power of data with OpenData Vector! 🚀 This MIT-licensed tool transforms vector search on object storage, making it easier to harness insights from your data. Ready to elevate your search capabilities? Let's dive in! #OpenData #VectorSearch #Innovation
Today’s AI tip: RAG helps AI answer with context, not just memory.
Learning this more deeply right now.
#AI #RAG #AIEngineering #RetrievalAugmentedGeneration #LLMs #ContextEngineering #Embeddings #VectorSearch #LearningInPublic #BuildInPublic #SoftwareEngineering #GitHub
When MinHashLSH-style search fits and sub-100ms ANN isn't needed, the lakehouse already has the primitives.
When sub-100ms ANN at billion-vector scale is needed → Hudi's VECTOR type + hudi_vector_search + Lance IVF-PQ.
Links ↓
#ApacheHudi #VectorSearch
Keyword search finds documents containing your words.
Semantic search finds documents containing your meaning.
Here's how it works 👇
https://t.co/SzBrR63vGO
#SemanticSearch #AI #MachineLearning #NLP #VectorSearch #DataScience #AIEngineering #SearchAI #Embeddings #GenerativeAI
Opinión: hablar de IA generativa sin entender índices vectoriales es construir sobre arena. 🏗️
Son la pieza que hace que buscar entre millones de vectores sea posible en tiempo real.
¿De acuerdo o exageramos?
👉 https://t.co/Rg8fiOL7fO
#IA #VectorSearch #Embeddings
8 vector databases. One interface. numpy → qdrant → weaviate → pgvector → milvus. Swap one line, rest stays the same. Weaviate hybrid at alpha=0.1 scored 0.9940.
https://t.co/yMaV6JJRmv
#RAG #VectorSearch
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