Fine-tuning embeddings 4 vector search
summary
OpenSearch users fine-tune embeddings for RAG by generating supervised pairs from their documents with the open‑sourced impar toolkit that uses generative LLMs, requiring only few hours of compute. Fine-tuning moves relevant queries and passages closer and boosts domain performance — examples show Precision@10 from 30% to 80% in chemical, legal and CV/job matching datasets. Hybrid combination risks polluting results (reciprocal rank fusion often reflects the weaker input).
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