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The widespread availability of search API's (both free and commercial) bringsthe promise of increased coverage and quality of search results for metasearchengines, while decreasing the maintenance costs of the crawling and indexinginfrastructures. However, merging strategies frequently comprise complexpipelines that require careful tuning, which is often overlooked in theliterature. In this work, we describe NeuralSearchX, a metasearch engine basedon a multi-purpose large reranking model to merge results and highlightsentences. Due to the homogeneity of our architecture, we could focus ouroptimization efforts on a single component. We compare our system withMicrosoft's Biomedical Search and show that our design choices led to a muchcost-effective system with competitive QPS while having close tostate-of-the-art results on a wide range of public benchmarks. Human evaluationon two domain-specific tasks shows that our retrieval system outperformedGoogle API by a large margin in terms of nDCG@10 scores. By describing ourarchitecture and implementation in detail, we hope that the community willbuild on our design choices. The system is available athttps://neuralsearchx.nsx.ai.

NeuralSearchX: Serving a Multi-billion-parameter Reranker for Multilingual Metasearch at a Low Cost