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  "aboutHtml": "<p>multi-qa-mpnet-base-dot-v1 je model Sentence Transformers, zasnovan za pridobivanje besedilnih odlomkov glede na vprašanje ali poizvedbo. Pri učenju je uporabil približno 215 milijonov parov vprašanje–odgovor. Vsak vhod postane 768-razsežen gosti vektor, to konkretno izdajo pa določata CLS-pooling in ocenjevanje s skalarnim produktom.</p><h4>Pripravite vprašanje in kandidatne odlomke</h4><p>Poizvedbo zapišite kot informacijsko potrebo, vnose korpusa pa pripravite kot čitljive besedilne odlomke. Vprašanje o spremembi naslova dostave lahko na primer primerjate z ločenimi odstavki o spremembah naslova, preklicu naročila in spremljanju dostave. V odlomku o spremembi naslova ohranite relevantne pogoje, vključno s tem, ali je bilo naročilo že odposlano.</p><p>Poizvedbo in odlomke kodirajte z istim modelom. Objavljeni primer to naredi neposredno, brez predpone query/document, in odlomke razvrsti po ocenah skalarnega produkta. Vsak vektor ohranite povezan z izvirnim odlomkom in identifikatorjem dokumenta, da razvrščeni rezultat lahko pokaže dejansko indeksirano gradivo.</p><h4>CLS-pooling in izdaja s skalarnim produktom</h4><p>Referenčni postopek modela vzame predstavitev prvega žetona, kar se imenuje CLS-pooling. Njegova tehnična tabela izrecno navaja, da embeddingi niso normalizirani, in kot ustrezno funkcijo ocenjevanja navede skalarni produkt. Ta postopek ohranite pri nastavljanju zbirke in strani poizvedb.</p><p>Normalizacija L2 teh vektorjev spremeni geometrijo njihovega ocenjevanja. Normalizirani profil all-mpnet-base-v2 uporablja drugačen postopek in je ločena kontrolna točka, čeprav deli družino MPNet in izhod s 768 komponentami. Konkretno izdajo in primerjalno funkcijo ohranite s shranjeno zbirko, namesto da ju izberete samo po dolžini vektorja.</p><h4>Omejitev dolžine in daljši odlomki</h4><p>Navedena omejitev vhoda je 512 podbesednih enot; daljše besedilo se skrajša. Proizvajalec opozarja tudi, da so učni vhodi dosegali samo 250 podbesednih enot in da daljše besedilo lahko deluje slabše. Sprejeta dolžina tokenizatorja in opozorilo o dolžini pri učenju zato opisujeta različni praktični meji.</p><p>Pri obsežnem članku pomoči pripravite kratke dele, ki odgovarjajo na konkretna vprašanja. Naslov ohranite tam, kjer določa temo, pravilo pa skupaj z izjemo, potrebno za pravilno branje. En celostranski vhod lahko s skrajšanjem izgubi poznejše gradivo, premajhen fragment pa lahko izgubi pogoj, zaradi katerega je uporaben.</p><h4>Razvrstitev uporabite pri iskanju in RAG</h4><p>Gosti vektorji podpirajo prvo stopnjo pridobivanja informacij. Vrnite kandidatne odlomke z najvišjimi ocenami in sklici na vire, nato preglejte, ali obravnavajo dejansko vprašanje. Pri RAG te odlomke kot izvorno gradivo podajte stopnji pisanja odgovora; pridobivanje samo ne sestavi odgovora.</p><p>Za izbiro števila rezultatov in oceno razvrstitve uporabite reprezentativna vprašanja z znanimi relevantnimi odlomki. Vključite primere s podobnim besediščem, a drugačnim namenom, na primer spremembo naslova in ogled trenutnega naslova. Ocene interpretirajte relativno glede na kandidate pri istem modelu in nastavitvi ocenjevanja, ne kot umerjene verjetnosti pravilnosti.</p><p>Koristni izhod te kontrolne točke je prostor embeddingov vprašanj in odlomkov za semantično iskanje. Njeni nenormalizirani vektorji, CLS-pooling, ocena skalarnega produkta in objavljeno opozorilo za dolge vhode so podrobnosti, ki jih je treba ohraniti pri pripravi in uporabi rezultatov.</p>",
  "aboutText": "multi-qa-mpnet-base-dot-v1 je model Sentence Transformers, zasnovan za pridobivanje besedilnih odlomkov glede na vprašanje ali poizvedbo. Pri učenju je uporabil približno 215 milijonov parov vprašanje–odgovor. Vsak vhod postane 768-razsežen gosti vektor, to konkretno izdajo pa določata CLS-pooling in ocenjevanje s skalarnim produktom.\n\n### Pripravite vprašanje in kandidatne odlomke\n\nPoizvedbo zapišite kot informacijsko potrebo, vnose korpusa pa pripravite kot čitljive besedilne odlomke. Vprašanje o spremembi naslova dostave lahko na primer primerjate z ločenimi odstavki o spremembah naslova, preklicu naročila in spremljanju dostave. V odlomku o spremembi naslova ohranite relevantne pogoje, vključno s tem, ali je bilo naročilo že odposlano.\n\nPoizvedbo in odlomke kodirajte z istim modelom. Objavljeni primer to naredi neposredno, brez predpone query/document, in odlomke razvrsti po ocenah skalarnega produkta. Vsak vektor ohranite povezan z izvirnim odlomkom in identifikatorjem dokumenta, da razvrščeni rezultat lahko pokaže dejansko indeksirano gradivo.\n\n### CLS-pooling in izdaja s skalarnim produktom\n\nReferenčni postopek modela vzame predstavitev prvega žetona, kar se imenuje CLS-pooling. Njegova tehnična tabela izrecno navaja, da embeddingi niso normalizirani, in kot ustrezno funkcijo ocenjevanja navede skalarni produkt. Ta postopek ohranite pri nastavljanju zbirke in strani poizvedb.\n\nNormalizacija L2 teh vektorjev spremeni geometrijo njihovega ocenjevanja. Normalizirani profil all-mpnet-base-v2 uporablja drugačen postopek in je ločena kontrolna točka, čeprav deli družino MPNet in izhod s 768 komponentami. Konkretno izdajo in primerjalno funkcijo ohranite s shranjeno zbirko, namesto da ju izberete samo po dolžini vektorja.\n\n### Omejitev dolžine in daljši odlomki\n\nNavedena omejitev vhoda je 512 podbesednih enot; daljše besedilo se skrajša. Proizvajalec opozarja tudi, da so učni vhodi dosegali samo 250 podbesednih enot in da daljše besedilo lahko deluje slabše. Sprejeta dolžina tokenizatorja in opozorilo o dolžini pri učenju zato opisujeta različni praktični meji.\n\nPri obsežnem članku pomoči pripravite kratke dele, ki odgovarjajo na konkretna vprašanja. Naslov ohranite tam, kjer določa temo, pravilo pa skupaj z izjemo, potrebno za pravilno branje. En celostranski vhod lahko s skrajšanjem izgubi poznejše gradivo, premajhen fragment pa lahko izgubi pogoj, zaradi katerega je uporaben.\n\n### Razvrstitev uporabite pri iskanju in RAG\n\nGosti vektorji podpirajo prvo stopnjo pridobivanja informacij. Vrnite kandidatne odlomke z najvišjimi ocenami in sklici na vire, nato preglejte, ali obravnavajo dejansko vprašanje. Pri RAG te odlomke kot izvorno gradivo podajte stopnji pisanja odgovora; pridobivanje samo ne sestavi odgovora.\n\nZa izbiro števila rezultatov in oceno razvrstitve uporabite reprezentativna vprašanja z znanimi relevantnimi odlomki. Vključite primere s podobnim besediščem, a drugačnim namenom, na primer spremembo naslova in ogled trenutnega naslova. Ocene interpretirajte relativno glede na kandidate pri istem modelu in nastavitvi ocenjevanja, ne kot umerjene verjetnosti pravilnosti.\n\nKoristni izhod te kontrolne točke je prostor embeddingov vprašanj in odlomkov za semantično iskanje. Njeni nenormalizirani vektorji, CLS-pooling, ocena skalarnega produkta in objavljeno opozorilo za dolge vhode so podrobnosti, ki jih je treba ohraniti pri pripravi in uporabi rezultatov.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Sentence Transformers: multi-qa-mpnet-base-dot-v1</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Zapri\">×</button></div><div class=\"idline\"><span>Sentence Transformers /</span><code>sentence-transformers/multi-qa-mpnet-base-dot-v1</code><button type=\"button\" id=\"copy-id\">Kopiraj ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Razdelki kartice modela\"><button type=\"button\" data-card-jump=\"overview\">Začetek</button><button type=\"button\" data-card-jump=\"methods\">Vhodi in metode</button><button type=\"button\" data-card-jump=\"pricing\">Cenik</button><button type=\"button\" data-card-jump=\"examples\">Primeri</button><button type=\"button\" data-card-jump=\"results\">Rezultati</button><button type=\"button\" data-card-jump=\"aliases\">Druga imena</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"Metode API\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|embeddings|/v1/embeddings|sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1\">Vektorske predstavitve</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Začetek</h3><p class=\"mdesc-full\">Sentence Transformers multi-qa-mpnet-base-dot-v1 vprašanja in odlomke kodira v 768-razsežne vektorje za semantično iskanje. Uporablja CLS-pooling in nenormalizirane embeddinge, namenjene ocenjevanju s skalarnim produktom, z omejitvijo vhoda 512 podbesednih enot.</p><dl class=\"card-io\" data-card-io><div><dt>Dovoljeni vhodi</dt><dd>Besedilo · token_ids</dd></div><div><dt>Rezultati</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>O modelu</summary><p class=\"note-s\" data-model-method-boundary>Ta opis zajema zmožnosti modela. Vhodi, izhodi in možnosti, ki so na voljo v Ailoot, so spodaj navedeni za izbrano metodo.</p><div class=\"mabout\" id=\"m-about\"><p>multi-qa-mpnet-base-dot-v1 je model Sentence Transformers, zasnovan za pridobivanje besedilnih odlomkov glede na vprašanje ali poizvedbo. Pri učenju je uporabil približno 215 milijonov parov vprašanje–odgovor. Vsak vhod postane 768-razsežen gosti vektor, to konkretno izdajo pa določata CLS-pooling in ocenjevanje s skalarnim produktom.</p><h4>Pripravite vprašanje in kandidatne odlomke</h4><p>Poizvedbo zapišite kot informacijsko potrebo, vnose korpusa pa pripravite kot čitljive besedilne odlomke. Vprašanje o spremembi naslova dostave lahko na primer primerjate z ločenimi odstavki o spremembah naslova, preklicu naročila in spremljanju dostave. V odlomku o spremembi naslova ohranite relevantne pogoje, vključno s tem, ali je bilo naročilo že odposlano.</p><p>Poizvedbo in odlomke kodirajte z istim modelom. Objavljeni primer to naredi neposredno, brez predpone query/document, in odlomke razvrsti po ocenah skalarnega produkta. Vsak vektor ohranite povezan z izvirnim odlomkom in identifikatorjem dokumenta, da razvrščeni rezultat lahko pokaže dejansko indeksirano gradivo.</p><h4>CLS-pooling in izdaja s skalarnim produktom</h4><p>Referenčni postopek modela vzame predstavitev prvega žetona, kar se imenuje CLS-pooling. Njegova tehnična tabela izrecno navaja, da embeddingi niso normalizirani, in kot ustrezno funkcijo ocenjevanja navede skalarni produkt. Ta postopek ohranite pri nastavljanju zbirke in strani poizvedb.</p><p>Normalizacija L2 teh vektorjev spremeni geometrijo njihovega ocenjevanja. Normalizirani profil all-mpnet-base-v2 uporablja drugačen postopek in je ločena kontrolna točka, čeprav deli družino MPNet in izhod s 768 komponentami. Konkretno izdajo in primerjalno funkcijo ohranite s shranjeno zbirko, namesto da ju izberete samo po dolžini vektorja.</p><h4>Omejitev dolžine in daljši odlomki</h4><p>Navedena omejitev vhoda je 512 podbesednih enot; daljše besedilo se skrajša. Proizvajalec opozarja tudi, da so učni vhodi dosegali samo 250 podbesednih enot in da daljše besedilo lahko deluje slabše. Sprejeta dolžina tokenizatorja in opozorilo o dolžini pri učenju zato opisujeta različni praktični meji.</p><p>Pri obsežnem članku pomoči pripravite kratke dele, ki odgovarjajo na konkretna vprašanja. Naslov ohranite tam, kjer določa temo, pravilo pa skupaj z izjemo, potrebno za pravilno branje. En celostranski vhod lahko s skrajšanjem izgubi poznejše gradivo, premajhen fragment pa lahko izgubi pogoj, zaradi katerega je uporaben.</p><h4>Razvrstitev uporabite pri iskanju in RAG</h4><p>Gosti vektorji podpirajo prvo stopnjo pridobivanja informacij. Vrnite kandidatne odlomke z najvišjimi ocenami in sklici na vire, nato preglejte, ali obravnavajo dejansko vprašanje. Pri RAG te odlomke kot izvorno gradivo podajte stopnji pisanja odgovora; pridobivanje samo ne sestavi odgovora.</p><p>Za izbiro števila rezultatov in oceno razvrstitve uporabite reprezentativna vprašanja z znanimi relevantnimi odlomki. Vključite primere s podobnim besediščem, a drugačnim namenom, na primer spremembo naslova in ogled trenutnega naslova. Ocene interpretirajte relativno glede na kandidate pri istem modelu in nastavitvi ocenjevanja, ne kot umerjene verjetnosti pravilnosti.</p><p>Koristni izhod te kontrolne točke je prostor embeddingov vprašanj in odlomkov za semantično iskanje. Njeni nenormalizirani vektorji, CLS-pooling, ocena skalarnega produkta in objavljeno opozorilo za dolge vhode so podrobnosti, ki jih je treba ohraniti pri pripravi in uporabi rezultatov.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Na voljo v Ailoot · vhodi in metode</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Vektorske predstavitve</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Vhod</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Izhod</dt><dd><code>embedding</code></dd></div><div><dt>Izhodni format</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Polje</th><th>Vrsta</th><th>Zahteva</th><th>Vrednosti in omejitve</th><th>Privzeto</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>string</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Obvezno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>sentence-transformers/multi-qa-mpnet-base-dot-v1</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Obvezno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Najmanjše število elementov: 1</div><div>Pogoji: Neprazen paket istovrstnih vhodnih podatkov. Identifikatorji žetonov so nenegativna cela števila.</div><div>Vhodni proračun: Največ: 512 · Besedilna enota: bajti UTF8 · Enota žetonov: elementi polja žetonov</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">encoding_format</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>string</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>float</div></td><td data-cell-label=\"Privzeto\" data-default=\"known\"><span class=\"card-cell-label\">Privzeto</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>integer</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Ni podprto</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Najmanj: 1</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>json_value</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Pogoji: Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>json_value</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Pogoji: Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>json_value</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Pogoji: Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>object</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Pogoji: provider.options je zavrnjen</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Kombinacije vhodnih gradiv</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Parametri in življenjski cikel metode</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Specifikacija</th></tr></thead><tbody><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>content_type</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>application/json</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>required</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>model, input</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>input_shapes</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>string, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>encoding_format</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>float</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>dimensions_override</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>false</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>max_input_budget_units</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>512</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>multimodal_objects</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>false</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>material_types</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>text, token_ids</td></tr></tbody></table></div><p>Življenjski cikel: <code>synchronous_json</code></p><p class=\"note-s\">Shranite indeks in dimenzijo vsakega vektorja.</p><p class=\"note-s\">Ime multimodalnega modela ne pomeni, da so na tej točno določeni poti dovoljeni slikovni objekti.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Cenik</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tarifni podatki ne dodajajo možnosti zahteve. Podprti parametri so navedeni pri izbrani metodi.</p><dl data-price-conditions><dt>Hitri način</dt><dd>Ni navedeno <span class=\"note-s\">Ko je izbran hitri način</span></dd><dt>Način flex</dt><dd>Ni navedeno <span class=\"note-s\">Ko je izbran način flex</span></dd><dt>Najvišja raven razmišljanja</dt><dd>Ni navedeno <span class=\"note-s\">Ko najvišja raven razmišljanja uporablja privzeto prilagoditev (default)</span></dd></dl></div><div data-declared-tariff-pricing><h4>Cene Ailoot</h4><h4>Cene po nastavitvah</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Nastavitev</th><th>Cenovna lestvica</th><th>Strošek</th></tr></thead><tbody><tr><th data-cell-label=\"Nastavitev\" scope=\"row\"><span class=\"card-cell-label\">Nastavitev</span>Osnovne cene za žetone</th><td data-cell-label=\"Cenovna lestvica\"><span class=\"card-cell-label\">Cenovna lestvica</span>Osnovna cena</td><td data-cell-label=\"Strošek\"><span class=\"card-cell-label\">Strošek</span><div><span>Vhod · Vhodni žetoni</span> <b>$0.00001</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Izhod · Izhodni žetoni</span> <b>$0</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Branje predpomnilnika</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Zapis v predpomnilnik</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Zapis v predpomnilnik, 1 ura</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot pred zahtevo rezervira ocenjeni znesek. Po rezultatu obračuna končni strošek na podlagi izmerjene porabe. Obračun pretakanja uporablja končni zapis porabe. <a href=\"/docs/sl/billing/reserve/\">Kako delujeta rezervacija in obračun →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Primeri zahtev</h3><h4>Ailoot Ultra · Vektorske predstavitve</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Primeri kode\"><button class=\"cg-tab\" id=\"model-code-2599-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2599-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-2599-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2599-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kopiraj kodo\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-2599-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-2599-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;sentence-transformers/multi-qa-mpnet-base-dot-v1&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-2599-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-2599-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;sentence-transformers/multi-qa-mpnet-base-dot-v1&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Prejmi in shrani rezultate</h3><h4>Ailoot Ultra · Vektorske predstavitve</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Ohrani vse dokončane rezultate</h5><p class=\"note-s\">Shranite vsak v celoti vrnjen odgovor, datoteko in del vsebine.</p><p class=\"note-s\">Shranite vsak vektor skupaj z njegovim indeksom. Število vektorjev mora biti enako številu vhodov; dimenzije morajo biti skladne.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Če je treba preveriti izvirni odgovor</h5><p class=\"note-s\">Končna točka za opravila ustvarjanja vektorskih predstavitev se ne uvaja.</p></div><details class=\"card-about\" data-output-fields><summary>Polja odgovora</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Polje</th><th>Vrsta</th></tr></thead><tbody><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span>string</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span>string</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span>integer</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span>finite float[]</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Napake in naslednji koraki</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Odgovor</th><th>Pogoj</th><th>Naslednji korak</th></tr></thead><tbody><tr><th data-cell-label=\"Odgovor\" scope=\"row\"><span class=\"card-cell-label\">Odgovor</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Pogoj\"><span class=\"card-cell-label\">Pogoj</span>Neveljavna obvezna polja, predstavitev gradiva ali vrednost parametra</td><td data-cell-label=\"Naslednji korak\"><span class=\"card-cell-label\">Naslednji korak</span>Pred novo zahtevo za ustvarjanje popravite navedena polja.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Druga imena</h3><h4>Druga imena modelov</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1&amp;route=Ultra&amp;caller=sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1&amp;fmt=embeddings\"><code>sentence-transformers/multi-qa-mpnet-base-dot-v1</code></a></span></p><h4>Druga imena poti metod</h4><p class=\"note-s\">Uporabite zgoraj navedene poti metod.</p><h4>Povezani ID-ji za klic</h4><p class=\"note-s\"><a href=\"#m=sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1&amp;route=Ultra&amp;caller=sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1&amp;fmt=embeddings\"><code>sentence-transformers/multi-qa-mpnet-base-dot-v1</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Neveljavna obvezna polja, predstavitev gradiva ali vrednost parametra",
                "http_status": 400,
                "recovery": "Pred novo zahtevo za ustvarjanje popravite navedena polja.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Končna točka za opravila ustvarjanja vektorskih predstavitev se ne uvaja."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Shranite vsak vektor skupaj z njegovim indeksom. Število vektorjev mora biti enako številu vhodov; dimenzije morajo biti skladne.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "sentence-transformers/multi-qa-mpnet-base-dot-v1"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 512,
                  "text_unit": "bajti UTF8",
                  "token_unit": "elementi polja žetonov"
                },
                "conditions": "Neprazen paket istovrstnih vhodnih podatkov. Identifikatorji žetonov so nenegativna cela števila.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options je zavrnjen",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Shranite indeks in dimenzijo vsakega vektorja.",
            "Ime multimodalnega modela ne pomeni, da so na tej točno določeni poti dovoljeni slikovni objekti."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 512,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
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          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
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        "ctx": null,
        "declared_pricing_profiles": [
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                "native_amount_usd_per_unit": "0.00000001",
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                "side": "in",
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            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"sentence-transformers/multi-qa-mpnet-base-dot-v1\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='sentence-transformers/multi-qa-mpnet-base-dot-v1',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.01,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
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        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
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            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
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                "side": "in",
                "unit": "one_hour_cache_creation_token"
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        "request_model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
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  ],
  "model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
  "nativeProvenance": {
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    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/sl/sentence-transformers-multi-qa-mpnet-base-dot-v1/",
    "description": "Sentence Transformers multi-qa-mpnet-base-dot-v1 vprašanja in odlomke kodira v 768-razsežne vektorje za semantično iskanje. Uporablja CLS-pooling in nenormalizirane embeddinge, namenjene ocenjevanju s skalarnim produktom, z omejitvijo vhoda 512 podbesednih enot.",
    "locale": "sl",
    "md_url": "https://ailoot.ai/models/sl/sentence-transformers-multi-qa-mpnet-base-dot-v1/index.md",
    "title": "Sentence Transformers: multi-qa-mpnet-base-dot-v1 — Ailoot",
    "url": "https://ailoot.ai/models/sl/sentence-transformers-multi-qa-mpnet-base-dot-v1/"
  },
  "rawModel": {
    "about": "multi-qa-mpnet-base-dot-v1 je model Sentence Transformers, zasnovan za pridobivanje besedilnih odlomkov glede na vprašanje ali poizvedbo. Pri učenju je uporabil približno 215 milijonov parov vprašanje–odgovor. Vsak vhod postane 768-razsežen gosti vektor, to konkretno izdajo pa določata CLS-pooling in ocenjevanje s skalarnim produktom.\n\n### Pripravite vprašanje in kandidatne odlomke\n\nPoizvedbo zapišite kot informacijsko potrebo, vnose korpusa pa pripravite kot čitljive besedilne odlomke. Vprašanje o spremembi naslova dostave lahko na primer primerjate z ločenimi odstavki o spremembah naslova, preklicu naročila in spremljanju dostave. V odlomku o spremembi naslova ohranite relevantne pogoje, vključno s tem, ali je bilo naročilo že odposlano.\n\nPoizvedbo in odlomke kodirajte z istim modelom. Objavljeni primer to naredi neposredno, brez predpone query/document, in odlomke razvrsti po ocenah skalarnega produkta. Vsak vektor ohranite povezan z izvirnim odlomkom in identifikatorjem dokumenta, da razvrščeni rezultat lahko pokaže dejansko indeksirano gradivo.\n\n### CLS-pooling in izdaja s skalarnim produktom\n\nReferenčni postopek modela vzame predstavitev prvega žetona, kar se imenuje CLS-pooling. Njegova tehnična tabela izrecno navaja, da embeddingi niso normalizirani, in kot ustrezno funkcijo ocenjevanja navede skalarni produkt. Ta postopek ohranite pri nastavljanju zbirke in strani poizvedb.\n\nNormalizacija L2 teh vektorjev spremeni geometrijo njihovega ocenjevanja. Normalizirani profil all-mpnet-base-v2 uporablja drugačen postopek in je ločena kontrolna točka, čeprav deli družino MPNet in izhod s 768 komponentami. Konkretno izdajo in primerjalno funkcijo ohranite s shranjeno zbirko, namesto da ju izberete samo po dolžini vektorja.\n\n### Omejitev dolžine in daljši odlomki\n\nNavedena omejitev vhoda je 512 podbesednih enot; daljše besedilo se skrajša. Proizvajalec opozarja tudi, da so učni vhodi dosegali samo 250 podbesednih enot in da daljše besedilo lahko deluje slabše. Sprejeta dolžina tokenizatorja in opozorilo o dolžini pri učenju zato opisujeta različni praktični meji.\n\nPri obsežnem članku pomoči pripravite kratke dele, ki odgovarjajo na konkretna vprašanja. Naslov ohranite tam, kjer določa temo, pravilo pa skupaj z izjemo, potrebno za pravilno branje. En celostranski vhod lahko s skrajšanjem izgubi poznejše gradivo, premajhen fragment pa lahko izgubi pogoj, zaradi katerega je uporaben.\n\n### Razvrstitev uporabite pri iskanju in RAG\n\nGosti vektorji podpirajo prvo stopnjo pridobivanja informacij. Vrnite kandidatne odlomke z najvišjimi ocenami in sklici na vire, nato preglejte, ali obravnavajo dejansko vprašanje. Pri RAG te odlomke kot izvorno gradivo podajte stopnji pisanja odgovora; pridobivanje samo ne sestavi odgovora.\n\nZa izbiro števila rezultatov in oceno razvrstitve uporabite reprezentativna vprašanja z znanimi relevantnimi odlomki. Vključite primere s podobnim besediščem, a drugačnim namenom, na primer spremembo naslova in ogled trenutnega naslova. Ocene interpretirajte relativno glede na kandidate pri istem modelu in nastavitvi ocenjevanja, ne kot umerjene verjetnosti pravilnosti.\n\nKoristni izhod te kontrolne točke je prostor embeddingov vprašanj in odlomkov za semantično iskanje. Njeni nenormalizirani vektorji, CLS-pooling, ocena skalarnega produkta in objavljeno opozorilo za dolge vhode so podrobnosti, ki jih je treba ohraniti pri pripravi in uporabi rezultatov.",
    "alias_of": null,
    "aliases": [],
    "author": "Sentence Transformers",
    "available": null,
    "canonical_model": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
    "capabilities": {
      "context_length": 512,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.00001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000001",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "Celoten cenik konfiguracije je v podrobnostih."
      }
    ],
    "ctx": null,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
    "input": [
      "text",
      "token_ids"
    ],
    "kind": "embedding",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Neveljavna obvezna polja, predstavitev gradiva ali vrednost parametra",
                "http_status": 400,
                "recovery": "Pred novo zahtevo za ustvarjanje popravite navedena polja.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Končna točka za opravila ustvarjanja vektorskih predstavitev se ne uvaja."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Shranite vsak vektor skupaj z njegovim indeksom. Število vektorjev mora biti enako številu vhodov; dimenzije morajo biti skladne.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "sentence-transformers/multi-qa-mpnet-base-dot-v1"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 512,
                  "text_unit": "bajti UTF8",
                  "token_unit": "elementi polja žetonov"
                },
                "conditions": "Neprazen paket istovrstnih vhodnih podatkov. Identifikatorji žetonov so nenegativna cela števila.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
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                "name": "dimensions",
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                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.",
                "default": null,
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                "enum": null,
                "enum_known": false,
                "name": "user",
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                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.",
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                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Izvor dovoljuje to polje metapodatkov JSON; natančen pomen pri ponudniku je odvisen od modela.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options je zavrnjen",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Shranite indeks in dimenzijo vsakega vektorja.",
            "Ime multimodalnega modela ne pomeni, da so na tej točno določeni poti dovoljeni slikovni objekti."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
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              "float"
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            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
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              "text",
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            "max_input_budget_units": 512,
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            "required": [
              "model",
              "input"
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          },
          "lifecycle": {
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            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
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        "ctx": null,
        "declared_pricing_profiles": [
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                "amount_unit": "usd_per_thousand_tokens",
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                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
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                "amount_unit": "usd_per_thousand_tokens",
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                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
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                "public_unit_label": "/ 1K tokens",
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                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
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                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
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                "rate_role": "typed_native_retail_rate",
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        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"sentence-transformers/multi-qa-mpnet-base-dot-v1\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
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            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
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            "value": "Not specified",
            "when": "When fast mode is selected"
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            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
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            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
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                "amount": "0.00001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
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                "label": "Input tokens",
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                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
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                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
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                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
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                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
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                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
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                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
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                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
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                "key": "cache_write_1h_price",
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                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
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                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
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        "request_model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
        "route_label": "Ultra",
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        "tiers": [],
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            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
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            "label": "Input tokens",
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            "billing_input": true,
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      "role": "historical-owner-input"
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    "schema": "ailoot-native-model-representations-v1"
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