RAG Benchmarks: Embedding Models, Vector DBs, Agentic RAG
RAG improves LLM reliability with external data sources. We benchmark the entire RAG pipeline: leading embedding models, top vector databases, and the latest agentic frameworks, all evaluated on their real-world performance.
Explore RAG Benchmarks: Embedding Models, Vector DBs, Agentic RAG
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…
RAG Evaluation Tools: Weights & Biases vs Ragas vs DeepEval
When a RAG pipeline retrieves the wrong context, the LLM confidently generates the wrong answer. Context relevance scorers are the primary defense. We benchmarked five tools across 1,460 questions and 14,600+ scored contexts under identical conditions: same judge model (GPT-4o), default configurations, and no custom prompts. Under standard conditions, WandB, TruLens, and Ragas emerged as…
Top 20+ Agentic RAG Frameworks
Agentic RAG enhances traditional RAG by boosting LLM performance and enabling greater specialization. We conducted a benchmark to assess its performance on routing between multiple databases and generating queries. Explore agentic RAG frameworks and libraries, key differences from standard RAG, benefits, and challenges to unlock their full potential. Agentic RAG benchmark: Multi-database routing and query…
Best RAG Tools, Frameworks, and Libraries
RAG improves LLM responses by grounding them in external data instead of just what the model memorized in training. We benchmarked the components a RAG system is built from and gathered the results in one place, with a practical guide to choosing each part of the stack. See our benchmark results for each RAG component,…
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…
Embedding Models: OpenAI vs Gemini vs Voyage
We benchmarked 15 English text-embedding models and a BM25 baseline on over 500 manually curated queries across three retrieval domains: legal contracts (CUAD), customer support (IBM TechQA), and healthcare (MedRAG PubMed). Voyage-3.5 ranks first overall. Perplexity Embed V1 0.6b reaches the upper-mid tier at the lowest price point in our benchmark. Embedding models benchmark results…
RAG Frameworks: LangChain vs LangGraph vs LlamaIndex
We benchmarked 5 RAG frameworks: LangChain, LangGraph, LlamaIndex, Haystack, and DSPy, by building the same agentic RAG workflow with standardized components: identical models (GPT-4.1-mini), embeddings (BGE-small), retriever (Qdrant), and tools (Tavily web search). This isolates each framework’s true overhead and token efficiency. RAG frameworks benchmark results The benchmark consisted of 100 queries, with each framework…
Reranker Benchmark: Top 8 Models Compared
We benchmarked 8 reranker models on ~145k English Amazon reviews to measure how much a reranking stage improves dense retrieval. We retrieved top-100 candidates with multilingual-e5-base, reranked them with each model, and evaluated the top-10 results against 300 queries, each referencing concrete details from its source review. The best reranker lifted Hit@1 from 62.67% to…