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RAG

RAG

RAG explores how to improve LLM reliability using external data sources. We benchmarked the entire 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 Weaviate 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 and Libraries

Explore RAG

Hybrid RAG: Boosting RAG Accuracy in 2025

RAGAug 12

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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RAGJul 24

Embedding Models: OpenAI vs Gemini vs Cohere in 2025

The effectiveness of any Retrieval-Augmented Generation (RAG) system depends on the precision of its retriever component. We benchmarked 11 leading text embedding models, including those from OpenAI, Gemini, Cohere, Snowflake, AWS, Mistral, and Voyage AI, using nearly 500,000 Amazon reviews. Our evaluation focused on each model’s ability to retrieve and rank the correct answer first.

RAGJul 25

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.

RAGJul 9

Top 20+ Agentic RAG  Frameworks in 2025

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.

RAGAug 13

Best RAG tools: Frameworks and Libraries in 2025

RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models with various chunk sizes to see what works best. Explore the RAG frameworks and tools, what RAG is, how it works, its benefits, and the current situation in the LLM landscape.