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
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.
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.
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.
Benchmark of 16 Best Open Source Embedding Models for RAG
Most embedding benchmarks measure semantic similarity. We measured correctness. We tested 16 open-source models, from 23M-parameter to 8B-parameter embeddings, on 490,000 Amazon product reviews, scoring each by whether it retrieved the right product review through exact ASIN matching, not just topically similar documents.
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.
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 and bfloat16 precision.
RAG Evaluation Tools: Weights & Biases vs Ragas vs DeepEval vs TruLens
Failures in Retrieval Augmented Generation systems occur not only because of hallucinations but more critically because of retrieval poisoning. In such cases, the retriever returns documents that share substantial lexical overlap with the query but do not contain the necessary information.
RAG Monitoring Tools Benchmark in 2026
We benchmarked leading RAG monitoring tools to assess their real-world impact on latency and developer experience. Our results show that: Results & Analysis The following table summarizes the latency performance of the RAG pipeline under different monitoring instrumentations: Key finding: All tools are production-ready All tested observability platforms introduce negligible latency overhead.
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.