emem-find-similar

emem-find-similar

This skill runs a nearest-neighbour search over the Tessera embedding field on emem.dev. Tessera is a 128-D learned multimodal vector that fuses Sentinel-2 optical, Sentinel-1 radar, and seasonality into one position-stable representation per cell per year. Two cells with cosine similarity >0.85 are usually the same physical archetype.

When to invoke

The user asks for analogues:

• "Find cities globally that look like Bangalore."
• "What other places have an urban canopy similar to Singapore?"
• "Show me regions with the same forest signature as the Western Ghats."
• "Compare Mumbai and Lagos by their Tessera embedding."

If the user wants exact-band matching (e.g., "all places with NDVI > 0.7"), this is the wrong skill — use query_region or compare_bands instead. This skill is vector cosine, not predicate filtering.

How to invoke

Step 1 — resolve the seed place to cell64

SEED_CELL=$(curl -sf -X POST https://emem.dev/v1/locate \
  -H 'content-type: application/json' \
  -d '{"q":"Bangalore, India"}' | jq -r '.cell64')
echo "seed cell: $SEED_CELL"

Step 2 — ensure the seed has a Tessera vector attested

/v1/find_similar returns 404 cid_not_found when the seed cell has no geotessera band attested on this responder. Materialise it first (idempotent if already present):

curl -sf -X POST https://emem.dev/v1/recall \
  -H 'content-type: application/json' \
  -d "{\"cell\":\"$SEED_CELL\",\"bands\":[\"geotessera\"]}" > /dev/null

Step 3 — query top-K neighbours

curl -sf -X POST https://emem.dev/v1/find_similar \
  -H 'content-type: application/json' \
  -d "{\"key\":\"$SEED_CELL\",\"k\":12}" \
  | jq '.neighbors[] | {cell, score, place: .place_label_cached, lat, lng}'

The response includes:

• neighbors[].cell — cell64 of the neighbour
• neighbors[].score — cosine similarity in [0, 1]
• neighbors[].lat, .lng — centre coords
• neighbors[].place_label_cached — cached human label if known
• neighbors[].band_used — almost always geotessera
• neighbors[].similarity_method — cosine (default) or hamming (if you set band: "geotessera.bin128")
• neighbors[].deep_recall_url — the /v1/recall payload that fetches the neighbour's full embedding for further drill-down