#RAG

AI…ColBERT model, with fewer searches than any baseline.

AI…Recall@1000, against 69.3% for nemotron-embed-8b.

AIA corpus indexed with the 9B model can be searched with 0.6B queries. That lifts ViDoRe v3 from 62.3% to 63.5% with no added query cost.

AIpplx-embed-v2-late keeps a 128-dimensional vector per token and scores with MaxSim, so each query token is matched to its closest token in the document.

AIA survey of 300 data, AI, and technology executives found only 34% of organizations' agentic AI projects reach production, with legacy systems, security concerns, and missing knowledge context as main obstacles. Production leaders, who advance 61% of projects beyond pilot, show stronger semantic knowledge capabilities. Most firms plan to invest in retrieval pipelines, AI-ready APIs, retrieval-augmented generation, and knowledge graphs.

AIIts queries, documents and capabilities are inspired by conversations with turbopuffer customers. It has 2,099 queries and 38,894 documents. We submitted for blind evaluation.

AIIt scores every document token against the query. Aggregated into chunk-level targets, those scores train the embedder to retrieve answer and supporting chunks.

AIContextual embedding models fix this by encoding the whole document once and pooling chunk vectors afterward. They are usually trained on one gold chunk per query.