rivera-edge 0.1.0

Rivera Edge — on-device semantic search: local embeddings + exact cosine retrieval in a single SQLite file. No server, no cloud calls.

Rivera Edge (beta)

On-device semantic search from the Rivera family: local embeddings (fastembed, CPU-only ONNX) + exact cosine retrieval, stored in a single SQLite file. No server. No cloud calls. No API key.

pip install rivera-edge

from rivera_edge import RiveraEdge

edge = RiveraEdge()                     # data at ~/.rivera-edge/edge.db
edge.add("notes", [
    {"id": "1", "text": "User prefers pytest over unittest"},
    {"id": "2", "text": "Deploys go through Railway", "metadata": {"type": "fact"}},
])
hits = edge.search("notes", "which test framework?")   # searchable instantly
print(hits[0]["text"], hits[0]["score"])

Same retrieval semantics as Rivera Cloud/On-Prem (exact, deterministic, instant ingestion) — scoped to a single process and disk. Namespaces, upsert by id, metadata, multi-namespace search, thresholds.

First call downloads the embedding model (~130 MB, cached). Beta: API may evolve; sync with Rivera Cloud is on the roadmap.

MIT © Mohit Bendale