The Excellence
Directory Platform Template

RAG evaluation metric assessing retriever's ability to rank relevant chunks higher than irrelevant ones, measuring context relevance and ranking quality for optimal retrieval.

RAG evaluation metric measuring whether retrieved context contains all information required to produce ideal output, assessing completeness and sufficiency of retrieval.

RAG evaluation metric measuring whether generated answers accurately align with retrieved context without hallucination, ensuring factual grounding of LLM responses.

The open‑source repository containing the implementation, configuration, and scripts of VectorDBBench, enabling users to run standardized benchmarks across multiple vector database systems locally or in CI.

An annual competition focused on similarity search and indexing algorithms, including approximate nearest neighbor methods and high-dimensional vector indexing, providing benchmarks and results relevant to vector database research.

ANN-Benchmarks is a benchmarking platform specifically for evaluating the performance of approximate nearest neighbor (ANN) search algorithms, which are foundational to vector database evaluation and comparison.

BEIR (Benchmarking IR) is a benchmark suite for evaluating information retrieval and vector search systems across multiple tasks and datasets. Useful for comparing vector database performance.

A collection of datasets curated by Intel Labs specifically for evaluating and benchmarking vector search algorithms and databases.

A massive text embedding benchmark for evaluating the quality of text embedding models, crucial for vector database applications.