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BAAI/bge-large-zh-v1.5 est l’édition chinoise de BAAI General Embedding v1.5. Elle convient à un index de recherche chinois, par exemple pour rapprocher une question client du paragraphe pertinent des conditions de retour. Elle produit un vecteur numérique de similarité, pas une réponse rédigée. La fiche officielle précise cette orientation linguistique et l’amélioration de la distribution des similarités en v1.5.

Ailoot accepte une chaîne ou un tableau de chaînes sur /v1/embeddings. Encodez passages et requêtes avec cette même édition et associez data[].embedding à data[].index. Pour une question courte recherchant un passage, BAAI conseille une instruction de recherche sur la requête ; les passages restent sans préfixe. Calibrez les seuils sur vos données : un score n’est pas la probabilité que le passage réponde. Consultez la fiche BAAI pour la convention de requête.

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BAAI/bge-large-zh-v1.5 est l’édition chinoise de BAAI General Embedding v1.5. Elle convient à un index de recherche chinois, par exemple pour rapprocher une question client du paragraphe pertinent des conditions de retour. Elle produit un vecteur numérique de similarité, pas une réponse rédigée. La fiche officielle précise cette orientation linguistique et l’amélioration de la distribution des similarités en v1.5.

Ailoot accepte une chaîne ou un tableau de chaînes sur /v1/embeddings. Encodez passages et requêtes avec cette même édition et associez data[].embedding à data[].index. Pour une question courte recherchant un passage, BAAI conseille une instruction de recherche sur la requête ; les passages restent sans préfixe. Calibrez les seuils sur vos données : un score n’est pas la probabilité que le passage réponde. Consultez la fiche BAAI pour la convention de requête.

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•

BAAI/bge-large-zh-v1.5

Autre /BAAI/bge-large-zh-v1.5

Pour commencer

Embeddings BAAI pour la recherche de passages en chinois, accessibles comme requêtes texte-vers-vecteur sur Ailoot.

Entrées acceptées
Texte
Résultats
embedding
À propos du modèle

Cette description présente les capacités du modèle. Les entrées, sorties et options disponibles dans Ailoot pour la méthode sélectionnée figurent ci-dessous.

BAAI/bge-large-zh-v1.5 est l’édition chinoise de BAAI General Embedding v1.5. Elle convient à un index de recherche chinois, par exemple pour rapprocher une question client du paragraphe pertinent des conditions de retour. Elle produit un vecteur numérique de similarité, pas une réponse rédigée. La fiche officielle précise cette orientation linguistique et l’amélioration de la distribution des similarités en v1.5.

Ailoot accepte une chaîne ou un tableau de chaînes sur /v1/embeddings. Encodez passages et requêtes avec cette même édition et associez data[].embedding à data[].index. Pour une question courte recherchant un passage, BAAI conseille une instruction de recherche sur la requête ; les passages restent sans préfixe. Calibrez les seuils sur vos données : un score n’est pas la probabilité que le passage réponde. Consultez la fiche BAAI pour la convention de requête.

Disponible dans Ailoot · entrées et méthodes

Ailoot Ultra · Embeddings POST /v1/embeddings
Entrée
text
Sortie
embedding
ChampTypeExigenceValeurs et limitesPar défaut
ChampmodelTypestringExigenceObligatoireValeurs et limites
BAAI/bge-large-zh-v1.5
Conditions: Utilisez exactement ce sélecteur de routage Ailoot.
Par défaut—
ChampinputTypestring or array<string>ExigenceObligatoireValeurs et limites
Conditions:
Par défaut—
Paramètres et cycle de vie de la méthode
ParamètreContrat
Paramètrecontent_typeContratapplication/json
ParamètrerequiredContratmodel, input
ParamètreoptionalContrat
Paramètrematerial_typesContrattext
ParamètreconditionsContratUtilisez exactement ce sélecteur de routage Ailoot.

Cycle de vie : synchronous_json

Tarifs

Les tarifs n’ajoutent pas d’options de requête. Les paramètres pris en charge figurent dans la méthode sélectionnée.

Mode rapide
Non précisé Lorsque le mode rapide est sélectionné
Mode Flex
Non précisé Lorsque le mode flex est sélectionné
Effort de raisonnement maximal
Non précisé Lorsque l’effort de raisonnement maximal utilise l’ajustement par défaut (default)

Tarifs Ailoot

Prix par configuration

ConfigurationBarème tarifaireCoût
ConfigurationTarifs de base des jetonsBarème tarifaireTarif de baseCoût
Entrée · Jetons en entrée $0.00004 / 1K jetons
Sortie · Jetons en sortie $0 / 1K jetons
Entrée · Lecture du cache — / 1K jetons
Entrée · Écriture dans le cache — / 1K jetons
Entrée · Écriture dans le cache, 1 h — / 1K jetons

Ailoot réserve un montant estimé avant une requête. Après le résultat, le montant final est débité selon l’utilisation mesurée. Pour le streaming, le montant final à débiter est calculé à partir du relevé final d’utilisation. Fonctionnement de la réservation et du débit →

Exemples de requêtes

Ailoot Ultra · Embeddings

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "BAAI/bge-large-zh-v1.5", "input": "The quick brown fox jumps over the lazy dog"}')\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\n# Submit once. Retain the complete result, original identifiers and every response header.\n

Recevoir et enregistrer les résultats

Alias

Alias du modèle

Ailoot Ultra BAAI/bge-large-zh-v1.5

Alias des chemins de méthode

Utilisez les chemins de méthode indiqués ci-dessus.

ID d’appel associés

BAAI/bge-large-zh-v1.5

", "method_identity": { "effective_request_model_id": "BAAI/bge-large-zh-v1.5", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "BAAI/bge-large-zh-v1.5", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|BAAI%2Fbge-large-zh-v1.5", "ordinal": 0, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "accepted_material_guidance": { "conditions": [ "Send text with the selected model; retain every returned vector and its index." ], "content_type": "application/json", "material_types": [ "text" ], "optional": [], "required": [ "model", "input" ] }, "all_results": "Retain the complete response and headers, original identifiers, every actual output and all usage/accounting fields. Save each unique owned asset and retain every role pointing to it.", "authentication": { "header": "Authorization", "scheme": "Bearer", "value": "$AILOOT_API_KEY" }, "errors": [ { "condition": "Un champ obligatoire, un sélecteur de modèle, un type ou une représentation des données d’entrée est invalide.", "http_status": 400, "recovery": "Examinez les détails de validation retournés avant de préparer une requête distincte.", "type": "invalid_request_error" }, { "condition": "La requête d’origine ou son résultat est inconnu ou nécessite une vérification.", "http_status": null, "recovery": "Conservez les identifiants et les en-têtes d’origine ; consultez uniquement les enregistrements d’état ou de bibliothèque pris en charge. Ne soumettez pas de nouveau la requête de création.", "type": null } ], "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/embeddings" }, "mode": "synchronous_json", "status": [], "unknown": "Conservez le X-Client-Request-ID d’origine, la réponse et les en-têtes. Un résultat inconnu ou à l’état result_pending_check n’autorise pas une deuxième création.", "unknown_no_replay": true }, "outputs": { "all_results": "Conservez la réponse et les en-têtes complets, les identifiants d’origine, chaque sortie réelle et tous les champs d’utilisation et de comptabilisation. Enregistrez chaque ressource unique appartenant à votre compte et conservez chaque rôle qui y renvoie.", "every_actual_output": true, "family": "embedding", "fields": [ { "name": "data[].index", "type": "integer" }, { "name": "data[].embedding", "type": "array" }, { "name": "model", "type": "string" }, { "name": "usage", "type": "object" } ] }, "parameters": [ { "conditions": "Utilisez exactement ce sélecteur de routage Ailoot.", "default_known": false, "enum": [ "BAAI/bge-large-zh-v1.5" ], "enum_known": true, "maximum_length": null, "name": "model", "required": true, "type": "string" }, { "conditions": "", "default_known": false, "enum_known": false, "maximum_length": null, "name": "input", "oneOf": [ { "minLength": 1, "type": "string" }, { "items": { "minLength": 1, "type": "string" }, "minItems": 1, "type": "array" } ], "required": true, "type": "string or array" } ], "request_schema": { "additionalProperties": true, "properties": { "input": { "oneOf": [ { "minLength": 1, "type": "string" }, { "items": { "minLength": 1, "type": "string" }, "minItems": 1, "type": "array" } ] }, "model": { "enum": [ "BAAI/bge-large-zh-v1.5" ], "type": "string" } }, "required": [ "model", "input" ], "type": "object" }, "schema_template": { "actual_file_preparation_required": false, "body": { "input": "The quick brown fox jumps over the lazy dog", "model": "BAAI/bge-large-zh-v1.5" }, "content_type": "application/json", "example_is_offline_content": true }, "usage": { "meter": "Use the accepted method price and actual recorded usage; the request reserve is an estimate. A billing unit does not add an input capability.", "preserve": "Keep all returned usage, accounting and request identifiers with every actual output.", "settlement": "Retain the original operation; reconcile its result and accounting once." } }, "input": { "conditions": [ "Utilisez exactement ce sélecteur de routage Ailoot." ], "content_type": "application/json", "material_types": [ "text" ], "optional": [], "required": [ "model", "input" ] }, "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/embeddings" }, "mode": "synchronous_json", "status": [], "unknown": "Keep the original X-Client-Request-ID, response and headers. An unknown or result_pending_check outcome does not authorize a second creation.", "unknown_no_replay": true }, "method": { "http": "POST", "path": "/v1/embeddings" }, "method_aliases": [], "output": { "all_results": "Retain the complete response and headers, original identifiers, every actual output and all usage/accounting fields. Save each unique owned asset and retain every role pointing to it.", "every_actual_output": true, "family": "embedding", "fields": [ { "name": "data[].index", "type": "integer" }, { "name": "data[].embedding", "type": "array" }, { "name": "model", "type": "string" }, { "name": "usage", "type": "object" } ] } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"BAAI/bge-large-zh-v1.5\", \"input\": \"The quick brown fox jumps over the lazy dog\"}')\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\n# Submit once. Retain the complete result, original identifiers and every response header.\n", "label": "curl · create once and retain the complete result", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "embeddings", "http": "POST", "in": 0.04, "input": [ "text" ], "label": "Embeddings", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "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": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "BAAI/bge-large-zh-v1.5", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } } ], "model_id": "BAAI/bge-large-zh-v1.5", "nativeProvenance": { "input_facts_canonical_sha256": "e85f42770ba4aeb74e93efd2844b1d64f24bcb45391587c8c4ba436143d7fd59", "input_html_sha256": "e7cece4db7500e865b6d5e63b9793149ff440c50e6c9da4e3582ce982727476c", "private_audit_sha256": "68c94d3a117b6b7fff0975d8611fd9fa467ed1882c2a0da1c96d4a859e811bef", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/fr-ca/baai-bge-large-zh-v1-5/", "description": "Embeddings BAAI pour la recherche de passages en chinois, accessibles comme requêtes texte-vers-vecteur sur Ailoot.", "locale": "fr-ca", "md_url": "https://ailoot.ai/models/fr-ca/baai-bge-large-zh-v1-5/index.md", "title": "BAAI/bge-large-zh-v1.5 — Ailoot", "url": "https://ailoot.ai/models/fr-ca/baai-bge-large-zh-v1-5/" }, "rawModel": { "about": "BAAI/bge-large-zh-v1.5 utilise /v1/embeddings avec model et input. Fournissez l’identifiant exact du modèle et une chaîne de texte non vide ou un tableau non vide de chaînes. La réponse JSON contient des entrées data avec index et embedding, ainsi que les informations model et usage.\n\nPour un index documentaire, préparez des passages cohérents et conservez vos identifiants de document et de segment avec les chaînes envoyées. Associez chaque vecteur renvoyé à son data[].index, sans utiliser le libellé du passage comme identifiant. Encodez une requête avec le même modèle lorsque votre application la compare au contenu indexé.\n\nLe point de terminaison renvoie les vecteurs de manière synchrone. Conservez toutes les entrées data et l’enregistrement usage complet avant de mettre l’index à jour. Vérifiez que les indices renvoyés couvrent le contenu envoyé, puis stockez le texte original et l’identifiant du modèle avec les vecteurs pour pouvoir remonter des résultats de recherche aux passages sources.", "alias_of": null, "aliases": [], "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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": "La tarification complète de la configuration figure dans les détails." } ], "ctx": null, "formats": [ "embeddings" ], "id": "BAAI/bge-large-zh-v1.5", "input": [ "text" ], "kind": "embedding", "max_out": null, "methods": [ { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "accepted_material_guidance": { "conditions": [ "Send text with the selected model; retain every returned vector and its index." ], "content_type": "application/json", "material_types": [ "text" ], "optional": [], "required": [ "model", "input" ] }, "all_results": "Retain the complete response and headers, original identifiers, every actual output and all usage/accounting fields. Save each unique owned asset and retain every role pointing to it.", "authentication": { "header": "Authorization", "scheme": "Bearer", "value": "$AILOOT_API_KEY" }, "errors": [ { "condition": "Un champ obligatoire, un sélecteur de modèle, un type ou une représentation des données d’entrée est invalide.", "http_status": 400, "recovery": "Examinez les détails de validation retournés avant de préparer une requête distincte.", "type": "invalid_request_error" }, { "condition": "La requête d’origine ou son résultat est inconnu ou nécessite une vérification.", "http_status": null, "recovery": "Conservez les identifiants et les en-têtes d’origine ; consultez uniquement les enregistrements d’état ou de bibliothèque pris en charge. Ne soumettez pas de nouveau la requête de création.", "type": null } ], "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/embeddings" }, "mode": "synchronous_json", "status": [], "unknown": "Conservez le X-Client-Request-ID d’origine, la réponse et les en-têtes. Un résultat inconnu ou à l’état result_pending_check n’autorise pas une deuxième création.", "unknown_no_replay": true }, "outputs": { "all_results": "Conservez la réponse et les en-têtes complets, les identifiants d’origine, chaque sortie réelle et tous les champs d’utilisation et de comptabilisation. Enregistrez chaque ressource unique appartenant à votre compte et conservez chaque rôle qui y renvoie.", "every_actual_output": true, "family": "embedding", "fields": [ { "name": "data[].index", "type": "integer" }, { "name": "data[].embedding", "type": "array" }, { "name": "model", "type": "string" }, { "name": "usage", "type": "object" } ] }, "parameters": [ { "conditions": "Utilisez exactement ce sélecteur de routage Ailoot.", "default_known": false, "enum": [ "BAAI/bge-large-zh-v1.5" ], "enum_known": true, "maximum_length": null, "name": "model", "required": true, "type": "string" }, { "conditions": "", "default_known": false, "enum_known": false, "maximum_length": null, "name": "input", "oneOf": [ { "minLength": 1, "type": "string" }, { "items": { "minLength": 1, "type": "string" }, "minItems": 1, "type": "array" } ], "required": true, "type": "string or array" } ], "request_schema": { "additionalProperties": true, "properties": { "input": { "oneOf": [ { "minLength": 1, "type": "string" }, { "items": { "minLength": 1, "type": "string" }, "minItems": 1, "type": "array" } ] }, "model": { "enum": [ "BAAI/bge-large-zh-v1.5" ], "type": "string" } }, "required": [ "model", "input" ], "type": "object" }, "schema_template": { "actual_file_preparation_required": false, "body": { "input": "The quick brown fox jumps over the lazy dog", "model": "BAAI/bge-large-zh-v1.5" }, "content_type": "application/json", "example_is_offline_content": true }, "usage": { "meter": "Use the accepted method price and actual recorded usage; the request reserve is an estimate. A billing unit does not add an input capability.", "preserve": "Keep all returned usage, accounting and request identifiers with every actual output.", "settlement": "Retain the original operation; reconcile its result and accounting once." } }, "input": { "conditions": [ "Utilisez exactement ce sélecteur de routage Ailoot." ], "content_type": "application/json", "material_types": [ "text" ], "optional": [], "required": [ "model", "input" ] }, "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/embeddings" }, "mode": "synchronous_json", "status": [], "unknown": "Keep the original X-Client-Request-ID, response and headers. An unknown or result_pending_check outcome does not authorize a second creation.", "unknown_no_replay": true }, "method": { "http": "POST", "path": "/v1/embeddings" }, "method_aliases": [], "output": { "all_results": "Retain the complete response and headers, original identifiers, every actual output and all usage/accounting fields. Save each unique owned asset and retain every role pointing to it.", "every_actual_output": true, "family": "embedding", "fields": [ { "name": "data[].index", "type": "integer" }, { "name": "data[].embedding", "type": "array" }, { "name": "model", "type": "string" }, { "name": "usage", "type": "object" } ] } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"BAAI/bge-large-zh-v1.5\", \"input\": \"The quick brown fox jumps over the lazy dog\"}')\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\n# Submit once. Retain the complete result, original identifiers and every response header.\n", "label": "curl · create once and retain the complete result", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "embeddings", "http": "POST", "in": 0.04, "input": [ "text" ], "label": "Embeddings", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "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": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "BAAI/bge-large-zh-v1.5", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "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", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "model_aliases": [ { "canonical_model_id": "BAAI/bge-large-zh-v1.5", "request_model_id": "BAAI/bge-large-zh-v1.5", "route_label": "Ultra" } ], "name": "BAAI/bge-large-zh-v1.5", "price": { "in": 0.04, "in_label": null, "out": 0, "out_label": null, "scope_label": "Token rates", "sort_in": 0.04, "sort_out": 0, "varies": false }, "short": "BAAI/bge-large-zh-v1.5 convertit une chaîne de texte ou une liste de chaînes en représentations vectorielles numériques avec Ailoot. Conservez chaque vecteur avec son indice d’entrée pour vos processus de recherche et d’indexation documentaire.", "title": "BAAI/bge-large-zh-v1.5" }, "renderer_schema": { "method_order": "Original DTO methods order; every exact route/format/path/caller survives.", "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers." }, "representation": "exact-public-dto-and-native-renderer-html", "representationNotes": { "current_reference": { "about_json_pointer": "/aboutHtml", "methods": [ { "contract_json_pointer": "/methods/0/rawMethod/contract", "html_json_pointer": "/methods/0/html", "method_ordinal": 0 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "Embeddings BAAI pour la recherche de passages en chinois, accessibles comme requêtes texte-vers-vecteur sur Ailoot.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "2da98d7b0a323da43c365d80405ac8210b681e6343cf1523b84255ca86368f4a", "renderer_sha256": "98cb81b9c4ef4923c22e586170e7cdf67056351148a1666fb03dd6a4a26716a3" }, "sourceRevision": "5da3e6fd888bd2251af1f0d0aada5662b14b741ad90f8488577f5fd925afde34" }