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.
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.
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.
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.
Explore RAG Benchmarks: Embedding Models, Vector DBs, Agentic RAG
RAG Frameworks: LangChain vs LangGraph vs LlamaIndex vs Haystack vs DSPy
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.
Best RAG Tools, Frameworks, and Libraries
RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models and separately tested various chunk sizes to determine what combinations work best for RAG systems. Explore top RAG frameworks and tools, learn what RAG is, how it works, its benefits, and its role in today’s LLM landscape.
Embedding Models: OpenAI vs Gemini vs Cohere
The effectiveness of any Retrieval-Augmented Generation (RAG) system depends on the precision of its retriever. We benchmarked 11 leading text embedding models, including those from OpenAI, Gemini, Cohere, Snowflake, AWS, Mistral, and Voyage AI, using ~500,000 Amazon reviews.
Top Vector Database for RAG: Qdrant vs Weaviate vs Pinecone
Vector databases power the retrieval layer in RAG workflows by storing document and query embeddings as high‑dimensional vectors. They enable fast similarity searches based on vector distances.
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.
Hybrid RAG: Boosting RAG Accuracy
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.