Pre-built FAISS semantic search indexes for all five corpora in the EPS Research RAG Corpus Series. Each index enables natural-language similarity search over structured astrophysical data without requiring re-embedding. Embedding model: sentence-transformers/all-MiniLM-L6-v2 (384-dimensional, L2 distance)Index type: faiss.IndexFlatL2 (exact search)Total vectors: 2,064 across 5 corpora Contents (15 files, 3 per corpus): Corpus Records FAISS file Corpus version Unified HI Rotation Curve Corpus 438 v7_sparc.faiss v7.0 Milky Way Globular Cluster Corpus 174 gc_corpus.faiss v1.3.2 Dwarf/Irregular HI Corpus 129 dwarf_corpus.faiss v1.0 IntZ Kinematic Corpus 1,292 intz_corpus.faiss v1b High-z Kinematic Corpus Z1 31 z1_corpus.faiss v1.0 Each corpus has three files: .faiss (binary index), _ids.json (ordered ID list mapping index row to object ID), _texts.json (curated summary strings that were embedded, for inspection and debugging). Quick start: python import faiss, json from sentence_transformers import SentenceTransformer index = faiss.read_index('v7_sparc.faiss') ids = json.load(open('v7_sparc_ids.json')) model = SentenceTransformer('all-MiniLM-L6-v2') q = model.encode(['dwarf irregular low surface brightness']).astype('float32') D, I = index.search(q, 5) print([ids[i] for i in I[0]]) Full platform: https://github.com/eps-research/rag-corpus-series