Interessante Erklärung von Databricks zu Vektordatenbanken.
🔑 **Kern-Einblick:** Traditionelle DBs finden exakte Treffer, Vektordatenbanken verstehen *Bedeutung* durch Embeddings – essenziell für semantische Suche & KI-Anwendungen.
- Handhabt hochdimensionale Vektoreffizient
- Beschleunigt Retrieval-Augmented Generation (RAG)
- Unterstützt komplexe Ähnlichkeitssuchen

#VectorDB #MachineLearning #KünstlicheIntelligenz #DataEngineering #Databricks

🔗 https://news.google.com/rss/articles/CBMiakFVX3lxTE9rWndNaVdDamV2VVlEQVFvWTVxaV92V0ZmODNvajdYdVFrZ1g1b1AtOFkwaW1QSzZuLWw0QTdUU1BmblVHOVBYazlzUXJDSHN4bmtYS0JaZkVCMjNBaGd6NFBrcHVFdjc1b0E?oc=5

everyone is talking about RAG, so I went down the rabbit hole and found the boring part that actually makes it work: retrieval and chunking

over the last months I've been studying and experimenting with vector databases and local LLMs, and I turned that into a three–part series on the Storyblok blog

today the last and most challenging piece is out: a step-by-step guide to building a fully local RAG pipeline with @weaviate + @ollama

in the article I show, with real code and a full repo linked:

• why hybrid search (BM25 + vectors) beats pure vector search
• how bad chunking quietly ruins most RAG systems
• why a smaller model + good retrieval often beats a huge (expensive) model with bad context
• how a structured CMS (like Storyblok) basically gives you chunking for free

stack:
• Weaviate for vectors and hybrid search
• Node.js for the glue
• Qwen 3.5 on Ollama running locally (but this works with cloud models too)

if you work on docs, DX, or AI features for content-heavy products, this might be a useful starting point

and since I'm still new to the topic, I'd really like feedback from you if you've built RAG systems in production

full article with code + repo:

How to build a RAG pipeline with Weaviate and Ollama – https://www.storyblok.com/mp/how-to-build-a-rag-pipeline-with-weaviate-and-ollama

#rag #vectordb #llm #semanticsearch

@OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

My #OpenSearch report highlights:
✅ Platform play
✅ Search variety
✅ Business criteria
✅ Security
And I'd add - it's OPEN SOURCE @linuxfoundation !!
https://opensearch.org/gigaom-radar-vector-report-2025/

#gigaom #vectorDB

My talk from #NDCLondon is now available on YouTube! 🇬🇧

http://youtube.com/watch?v=N2kn4UybFBI&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=1&t=9s

@OpenSearchProject @linuxfoundation
#vectorDB #OpenSearch #opensource #search #GenAI #OpenSearchAmbassador

Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍

One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usage

https://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/

#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

Chunking: an essential concept to understand for Retrieval-Augmented Generation (#RAG). It is the process of dividing large documents into smaller, manageable segments called “chunks.” Effective chunking preserves semantic meaning while ensuring content fits within model context limits.

Proper chunking is essential, as it directly affects retrieval quality. Well-structured chunks improve precision and support more accurate responses.



#OpenSource #devops #vectordb #programming #vector #search