A developer fine-tunes a custom embedding/reranking model on PAI, deploys it via Bailian as a managed inference endpoint, integrates it with OpenSearch for neural reranking, and additionally configures OpenSearch's native relevance features (BM25 weights, tokenization rules, synonym dictionaries, NER corrections) to create a comprehensive hybrid search ranking system.
A developer fine-tunes a custom embedding/reranking model on PAI, deploys it via Bailian as a managed inference endpoint, integrates it with OpenSearch for neural reranking, and additionally configures OpenSearch's native relevance features (BM25 weights, tokenization rules, synonym dictionaries, NER corrections) to create a comprehensive hybrid search ranking system.
See _combos/fine-tune-model-deploy-enhance-search-1bc7dd.
See _combos/custom-search-relevance-model-pipeline-1d5c69.
See es/es-optimize-results.
See opensearch/opensearch-optimize-relevance.
Q: How can I build an end-to-end search relevance pipeline that fine-tunes a model and fully optimizes OpenSearch? A: You can build this pipeline by fine-tuning a custom embedding or reranking model on PAI, deploying it through Bailian as a managed inference endpoint, and integrating it with OpenSearch alongside native relevance configurations. This setup enables neural reranking combined with OpenSearch features such as BM25 weights, tokenization rules, synonym dictionaries, and NER corrections to create a comprehensive hybrid search ranking system.