2 posts tagged with "rag"

We benchmarked OGX's RAG pipeline against OpenAI's file search across four BEIR retrieval datasets, MultiHOP RAG, and Doc2Dial. The results: OGX hybrid search beats OpenAI on 3 of 4 BEIR datasets, with up to 29.6% higher nDCG@10 on argument retrieval. Pair it with Gemma 31B and you get end-to-end RAG that exceeds GPT-4.1 by 81% on multi-hop reasoning, all running on your own infrastructure.

This isn't a synthetic demo. These are standard academic benchmarks, measured end-to-end through the same OpenAI-compatible APIs you'd use in production.

The Responses API is rapidly emerging as one of the most influential interfaces for building AI agents. It handles multi-step reasoning, tool orchestration, and conversational state in a single interaction, which is a big improvement over the manual orchestration loops that developers had to build on top of chat completion APIs. OGX's implementation of the Responses API brings these capabilities to the open source world, where you can choose your own models and run on your own infrastructure.

This post covers why the Responses API matters, what OGX's implementation enables, and how it connects to the broader move toward open agent standards like Open Responses.