RAG Benchmarks: Embedding Models, Vector DBs and Rerankers
RAG improves LLM reliability by grounding responses in external data. We benchmark the RAG stack across embedding models, rerankers, vector databases, frameworks and evaluation tools on retrieval accuracy, latency and quality.
Explore RAG Benchmarks: Embedding Models, Vector DBs and Rerankers
- RAGBenchmarkAug 14
Multimodal Embedding Models: Apple vs Meta vs OpenAI
Multimodal embedding models excel at identifying objects but struggle with relationships. Current models struggle to distinguish “phone on a map” from “map on a phone.” We benchmarked 7 leading models across MS-COCO and Winoground to measure this specific limitation. To ensure a fair comparison, we evaluated every model under identical conditions using NVIDIA A40 hardware…
- Vector DBBenchmarkJul 20
Vector Database Sizing and Selection Calculator
The practical question behind a self-hosted vector database for RAG is which engine fits a given server, and which one the workload rules out. The calculator below answers both, from our benchmark of seven self-hosted vector databases run at matched recall on identical embeddings. Five checkboxes at the top of the calculator name five common…
- RAGBenchmarkJun 30
Top 10 Multilingual Embedding Models for RAG
We benchmarked 10 multilingual embedding models on ~606k Amazon reviews across 6 languages (German, English, Spanish, French, Japanese, Chinese). We generated 1,800 queries (300 per language), each referencing concrete details from its source review. Models trained for search (query vs document separation) outperform larger models trained for general text similarity: e5_base (110M params) outperforms models…