Demos

Every demo below is a live app built on Layer that reimplements nothing. Each composes shipped gateway features — routing, hybrid text fusion, fuzzy matching, local embedding serving, pipelines, snapshots, and the function runtime — over a different corpus, and makes the gateway’s behavior legible in the UI. They are also the fastest way to see what the gateway does without standing up a cluster.

Demo	What it shows	Corpus
shelf	The query router, made legible	Books
chart	Query routing on clinical search, with a number	PMC-Patients case reports
hybrid-text	Hybrid text fusion, proven with qrels	BEIR/SciFact abstracts
wiki	Auto routing plus CPU-only Lattice embedding, at scale	Simple English Wikipedia
lens	Text-to-image search with zero GPUs	Wikimedia Commons Quality images
shop	Everything together — an end-to-end app	Amazon product catalog

These deployed applications demonstrate retrieval behavior. Running their full workloads can require external stores, model artifacts, and data preparation; they are separate from the local Compose quickstart.

shelf — book search that shows its routing

Live: shelf.hevlayer.com · Source: github.com/hev/shelf

One search box, three routes. Type an author, a title, or a vibe; the gateway’s Auto rank expression picks keyword (hybrid_text), semantic, or a fused blend from the shape of the query, and shelf renders that decision as a badge with the reason. The routing policy keys on token count, so the canned chips visibly change route as the query gets longer. This is the text-native routing showcase: it makes the query router decision the hero, not a footnote.

Built on the query router (Auto), hybrid text fusion, and fuzzy matching.

chart — clinical patient-notes search that shows its routing

Live: chart.hevlayer.com

The same routing hero on the corpus with the sharpest bimodal query distribution there is: clinicians search both by exact token (metformin 500mg, CABG, aspirn) and by clinical picture (elderly woman with progressive dyspnea and bilateral lower-extremity edema). chart is the first Layer demo with real relevance judgments — PMC-Patients ReCDS qrels — so the routing and hybrid claims are measured, not asserted. Behind the search box, an open-weight Gemma cascade (vLLM, scale-to-zero on the GPU pool) reads each note once and extracts clinical events and facet labels: the function runtime showcase.

The corpus is published, de-identified case reports (PMC-Patients, CC-BY-NC-SA). It is a search demo — not raw EHR, and not clinical advice.

Built on the query router, hybrid text fusion with fuzzy matching, pipelines, the function runtime, and snapshots.

hybrid-text — hybrid text fusion over SciFact

Live: hybrid-text.hevlayer.com · Source: github.com/hev/hybrid-text-fusion-demo

The eval-shaped sibling of the routing demos, over ~5,000 scientific abstracts from BEIR/SciFact. One query string fans out into a full-input BM25 leg plus one fuzzy leg per token, fused by reciprocal rank fusion — so results survive typos and morphological variants without losing BM25’s signal. It is purely lexical: no embeddings, no GPU, no vector index. SciFact ships qrels, so the UI flags known-relevant abstracts and the demo scores nDCG@10 / recall@10; every search also shows its gateway round-trip time and a fusion inspector (tokens, legs, RRF constant).

Built on hybrid text fusion and fuzzy matching.

wiki — all of Simple English Wikipedia, routed and embedded on CPU

Live: wiki.hevlayer.com · Source: github.com/hev/wiki

The routing hero at corpus scale: one Auto query over all 283,997 Simple English Wikipedia articles (1.74M paragraph rows) routes each search to full-text, semantic, or a fused RRF blend, and the UI renders the gateway’s routing echo beside every result. The semantic leg is the Lattice showcase — the whole corpus embedded through prefer: lattice, an ~8 MB int4 lookup-table artifact served in-process on the gateway CPU, with performance.embedding_ms and embedding_tokens echoed whenever the chosen route embeds. No GPU anywhere in the write or query path.

Built on the query router, hybrid text fusion, and local embedding serving (prefer: lattice).

lens — text-to-image search with zero GPUs

Live: lens.hevlayer.com · Source: github.com/hev/lens

Cross-modal search over Wikimedia Commons Quality images: type sunset over water, get sunsets. The schema is two lines — a string image_url attribute with a local CLIP embedding profile. The gateway fetches each image and runs CLIP’s image tower in-process on CPU at write time, then embeds query text with the same checkpoint’s text tower at query time. The app posts writes and queries and renders the echo; it contains no embedding, tokenizer, or image-preprocessing code, and there is no GPU worker or autoscaler pool anywhere in the path. Every result pairs the fixed serving contract (prefer: local, gateway CPU) with the live performance.embedding_ms echo, and carries its Commons attribution and license.

Built on local CLIP serving (serving.prefer: local, modality: image) and full-runtime schema configuration.