| name | durindoor-embeddings |
| description | Generate vector embeddings through DurinDoor using a model discovered from /v1/models/embedding. |
DurinDoor Embeddings
Discover
curl -H "Authorization: Bearer $DURINDOOR_KEY" "$DURINDOOR_URL/v1/models/embedding" | jq -r '.data[].id'
MODEL_ID="$(curl -s -H "Authorization: Bearer $DURINDOOR_KEY" "$DURINDOOR_URL/v1/models/embedding" | jq -r '.data[0].id')"
curl -H "Authorization: Bearer $DURINDOOR_KEY" "$DURINDOOR_URL/v1/models/info?id=$MODEL_ID"
Embed text
curl -X POST "$DURINDOOR_URL/v1/embeddings" \
-H "Authorization: Bearer $DURINDOOR_KEY" \
-H "Content-Type: application/json" \
-d "{\"model\":\"$MODEL_ID\",\"input\":[\"hello\",\"world\"]}"
input accepts a string or array. Optional dimensions, encoding format, and batch limits depend on the selected model. The response uses OpenAI-compatible data[].embedding arrays.
Reference: https://github.com/bloodf/durindoor/blob/main/docs/reference/api.md