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.

Embedding Models Benchmark

We benchmarked 11 leading text embedding models, including offerings from OpenAI, Gemini, Cohere, Snowflake, AWS, Mistral, and Voyage AI. Using nearly 500,000 Amazon reviews, our aim was to assess each model's ability to accurately retrieve and rank the correct answer, while also considering their cost-effectiveness.

Read OpenAI vs Gemini vs Cohere

Vector Databases Benchmark

We benchmarked 6 top vector databases for RAG to find the best option. Our tests evaluated pricing, performance, and features to determine which platform offers the most efficient similarity searches for RAG applications.

Read Qdrant vs Pinecone

Agentic RAG Benchmark

We developed a benchmark to evaluate Agentic RAG's ability to route queries across multiple databases and generate accurate queries. The system demonstrates autonomous reasoning by analyzing user queries, selecting the appropriate database from multiple options, and generating semantically correct queries to retrieve relevant information from distributed enterprise data sources.

Read agentic RAG frameworks

RAG Tools and Frameworks Benchmark

We benchmarked a variety of RAG frameworks and libraries. We covered the current landscape of RAG tools, comparing embedding models, chunk sizes, and the overall performance of top RAG systems.

Read RAG frameworks & libraries

Multimodal Embedding Models: Apple vs Meta vs OpenAI

We benchmarked 7 leading multimodal embedding models across MS-COCO and Winoground to measure this specific limitation.

Compare multimodal embedding models

Explore RAG Benchmarks: Embedding Models, Vector DBs, Agentic RAG

Hybrid RAG: Boosting RAG Accuracy

RAGDec 25

Dense vector search is excellent at capturing semantic intent, but it often struggles with queries that demand high keyword accuracy. To quantify this gap, we benchmarked a standard dense-only retriever against a hybrid RAG system that incorporates SPLADE sparse vectors.

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RAG Benchmarks: Embedding Models, Vector DBs, Agentic RAG

Embedding Models Benchmark

Vector Databases Benchmark

Agentic RAG Benchmark

RAG Tools and Frameworks Benchmark

Multimodal Embedding Models: Apple vs Meta vs OpenAI

Explore RAG Benchmarks: Embedding Models, Vector DBs, Agentic RAG

Hybrid RAG: Boosting RAG Accuracy

FAQ

Embedding Models Benchmark

Vector Databases Benchmark

Agentic RAG Benchmark

RAG Tools and Frameworks Benchmark

Multimodal Embedding Models: Apple vs Meta vs OpenAI