RAG
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.
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 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.
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.