Abhishek T.

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Building a RAG pipeline usually feels like assembling a 1,000-piece puzzle where half the pieces are from a different box. You've got your chunking logic, your embedding model, your vector store, and your LLM orchestration. 🧩

We decided to simplify the stack. One engine. One query. Total control. 🚀

It’s about reducing the "glue code" that slows down production systems. With the latest update to Vespa, you can now handle the entire retrieval logic inside the application:

⚡ Integrated embedding inference: no more API hops for your vectors

🔍 Hybrid search by default: combine BM25 and semantic ranking in one go

🛠️ Custom ranking expressions: tune your relevance without redeploying

📈 Scalable to billions of docs: because "it works on my laptop" isn't enough

The result? Lower latency, fewer moving parts, and a happier engineering team.

We’re seeing users cut their infrastructure costs by 40% just by removing the middleman services. 📉

If you’re tired of managing a "pile of glue," check out the new architecture guide. Link in the first comment! 👇

#VespaAI #SearchEngine #RAG #AIEngineering #VectorDatabase

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