{ "aboutHtml": "

Pilih Baichuan-Text-Embedding dalam permintaan /v1/embeddings. Medan input yang wajib menerima rentetan tidak kosong atau tatasusunan rentetan tidak kosong, membolehkan satu petikan dan koleksi yang disediakan menggunakan titik akhir yang sama. Hasil JSON merangkumi data[].embedding dan data[].index, bersama model pilihan dan usage.

Untuk koleksi pengetahuan, bahagikan dokumen sumber kepada petikan yang mengekalkan takrif dan konteks yang diperlukan oleh pembaca kemudian. Urus ID dokumen dalam aplikasi anda dan padankan setiap vektor dengan kedudukannya semasa dihantar. Aliran kerja carian boleh mengekod pertanyaan pengguna dengan model yang sama dan menyimpan petikan sumber bersama setiap hasil yang ditemui.

Simpan respons segerak yang lengkap sebelum merekodkan vektor ke storan. Semak pemetaan input kepada indeks, kekalkan semua vektor yang dipulangkan dan simpan usage bersama operasi. Penggunaan identiti model yang tepat untuk pembenaman tersimpan menjelaskan koleksi dan perwakilan pertanyaan yang saling sepadan.

", "aboutText": "Pilih Baichuan-Text-Embedding dalam permintaan /v1/embeddings. Medan input yang wajib menerima rentetan tidak kosong atau tatasusunan rentetan tidak kosong, membolehkan satu petikan dan koleksi yang disediakan menggunakan titik akhir yang sama. Hasil JSON merangkumi data[].embedding dan data[].index, bersama model pilihan dan usage.\n\nUntuk koleksi pengetahuan, bahagikan dokumen sumber kepada petikan yang mengekalkan takrif dan konteks yang diperlukan oleh pembaca kemudian. Urus ID dokumen dalam aplikasi anda dan padankan setiap vektor dengan kedudukannya semasa dihantar. Aliran kerja carian boleh mengekod pertanyaan pengguna dengan model yang sama dan menyimpan petikan sumber bersama setiap hasil yang ditemui.\n\nSimpan respons segerak yang lengkap sebelum merekodkan vektor ke storan. Semak pemetaan input kepada indeks, kekalkan semua vektor yang dipulangkan dan simpan usage bersama operasi. Penggunaan identiti model yang tepat untuk pembenaman tersimpan menjelaskan koleksi dan perwakilan pertanyaan yang saling sepadan.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "ebfba77e1fa824eca0695830800a287157e0707a1208b9098a50fcf02462ffab", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "ms", "methods": [ { "html": "
•

Baichuan-Text-Embedding

Lain-lain /Baichuan-Text-Embedding

Pengenalan

Baichuan-Text-Embedding memulangkan vektor berangka bagi teks yang diberikan melalui API pembenaman Ailoot. Hantar satu petikan atau senarai dan simpan indeks respons semasa membina koleksi yang boleh dicari.

Input yang diterima
Teks
Hasil
embedding
Tentang model

Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.

Pilih Baichuan-Text-Embedding dalam permintaan /v1/embeddings. Medan input yang wajib menerima rentetan tidak kosong atau tatasusunan rentetan tidak kosong, membolehkan satu petikan dan koleksi yang disediakan menggunakan titik akhir yang sama. Hasil JSON merangkumi data[].embedding dan data[].index, bersama model pilihan dan usage.

Untuk koleksi pengetahuan, bahagikan dokumen sumber kepada petikan yang mengekalkan takrif dan konteks yang diperlukan oleh pembaca kemudian. Urus ID dokumen dalam aplikasi anda dan padankan setiap vektor dengan kedudukannya semasa dihantar. Aliran kerja carian boleh mengekod pertanyaan pengguna dengan model yang sama dan menyimpan petikan sumber bersama setiap hasil yang ditemui.

Simpan respons segerak yang lengkap sebelum merekodkan vektor ke storan. Semak pemetaan input kepada indeks, kekalkan semua vektor yang dipulangkan dan simpan usage bersama operasi. Penggunaan identiti model yang tepat untuk pembenaman tersimpan menjelaskan koleksi dan perwakilan pertanyaan yang saling sepadan.

Tersedia di Ailoot · input & kaedah

Ailoot Ultra · Embeddings POST /v1/embeddings
Input
text
Output
embedding
MedanJenisKeperluanNilai & hadLalai
MedanmodelJenisstringKeperluanDiperlukanNilai & had
Baichuan-Text-Embedding
Syarat: Gunakan pemilih penghalaan Ailoot ini tepat seperti yang dinyatakan.
Lalai—
MedaninputJenisstring or array<string>KeperluanDiperlukanNilai & had
Syarat:
Lalai—
Parameter kaedah dan kitar hayat
ParameterKontrak
Parametercontent_typeKontrakapplication/json
ParameterrequiredKontrakmodel, input
ParameteroptionalKontrak
Parametermaterial_typesKontraktext
ParameterconditionsKontrakGunakan pemilih penghalaan Ailoot ini tepat seperti yang dinyatakan.

Kitar hayat: synchronous_json

Pelan harga

Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.

Mod pantas
Tidak dinyatakan Apabila mod pantas dipilih
Mod flex
Tidak dinyatakan Apabila mod flex dipilih
Usaha penaakulan maksimum
Tidak dinyatakan Apabila usaha penaakulan maksimum menggunakan pelarasan default

Kadar Ailoot

Harga mengikut konfigurasi

KonfigurasiJadual kadarCaj
KonfigurasiKadar asas tokenJadual kadarKadar asasCaj
Input · Token input $0.00016 / 1K token
Output · Token output $0 / 1K token
Input · Bacaan cache — / 1K token
Input · Penulisan cache — / 1K token
Input · Penulisan cache, 1 jam — / 1K token

Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →

Contoh permintaan

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": "Baichuan-Text-Embedding", "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

Terima dan simpan hasil

Alias

Alias model

Ailoot Ultra Baichuan-Text-Embedding

Alias laluan kaedah

Gunakan laluan kaedah yang dipaparkan di atas.

ID pemanggil berkaitan

Baichuan-Text-Embedding

", "method_identity": { "effective_request_model_id": "Baichuan-Text-Embedding", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "Baichuan-Text-Embedding", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|Baichuan-Text-Embedding", "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": "Medan wajib, pemilih model, jenis atau bentuk input tidak sah.", "http_status": 400, "recovery": "Semak butiran pengesahan yang dikembalikan sebelum menyediakan permintaan berasingan.", "type": "invalid_request_error" }, { "condition": "Permintaan atau hasil asal tidak diketahui atau memerlukan semakan.", "http_status": null, "recovery": "Simpan pengecam dan pengepala asal; baca hanya rekod status/pustaka yang disokong. Jangan hantar semula permintaan penciptaan.", "type": null } ], "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/embeddings" }, "mode": "synchronous_json", "status": [], "unknown": "Simpan X-Client-Request-ID asal, respons dan pengepala. Hasil yang tidak diketahui atau result_pending_check tidak membenarkan penciptaan kedua.", "unknown_no_replay": true }, "outputs": { "all_results": "Simpan respons dan pengepala lengkap, pengecam asal, setiap output sebenar dan semua medan penggunaan/perakaunan. Simpan setiap aset unik milik anda dan kekalkan setiap peranan yang merujuk kepadanya.", "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": "Gunakan pemilih penghalaan Ailoot ini tepat seperti yang dinyatakan.", "default_known": false, "enum": [ "Baichuan-Text-Embedding" ], "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": [ "Baichuan-Text-Embedding" ], "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": "Baichuan-Text-Embedding" }, "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": [ "Gunakan pemilih penghalaan Ailoot ini tepat seperti yang dinyatakan." ], "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": "0.00016000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000016", "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\": \"Baichuan-Text-Embedding\", \"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": 0.16, "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": "0.00016000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000016", "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": "Baichuan-Text-Embedding", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00016000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000016", "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": "Baichuan-Text-Embedding", "nativeProvenance": { "input_facts_canonical_sha256": "682b3f4530e6487981022f5b452145b10f18a658adde13b3bd62a60cadeaaa94", "input_html_sha256": "203e670b441a56184f285f96067e715b1fbd664079ffb261c89eae0fe65b1083", "private_audit_sha256": "1c69bcba3f790b2b156d961ec44f1fe55e092f6e4c142ef90d7ba9f4663e7f73", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/ms/baichuan-text-embedding/", "description": "Baichuan-Text-Embedding memulangkan vektor berangka bagi teks yang diberikan melalui API pembenaman Ailoot. Hantar satu petikan atau senarai dan simpan indeks respons semasa membina koleksi yang boleh dicari.", "locale": "ms", "md_url": "https://ailoot.ai/models/ms/baichuan-text-embedding/index.md", "title": "Baichuan-Text-Embedding — Ailoot", "url": "https://ailoot.ai/models/ms/baichuan-text-embedding/" }, "rawModel": { "about": "Pilih Baichuan-Text-Embedding dalam permintaan /v1/embeddings. Medan input yang wajib menerima rentetan tidak kosong atau tatasusunan rentetan tidak kosong, membolehkan satu petikan dan koleksi yang disediakan menggunakan titik akhir yang sama. Hasil JSON merangkumi data[].embedding dan data[].index, bersama model pilihan dan usage.\n\nUntuk koleksi pengetahuan, bahagikan dokumen sumber kepada petikan yang mengekalkan takrif dan konteks yang diperlukan oleh pembaca kemudian. Urus ID dokumen dalam aplikasi anda dan padankan setiap vektor dengan kedudukannya semasa dihantar. Aliran kerja carian boleh mengekod pertanyaan pengguna dengan model yang sama dan menyimpan petikan sumber bersama setiap hasil yang ditemui.\n\nSimpan respons segerak yang lengkap sebelum merekodkan vektor ke storan. Semak pemetaan input kepada indeks, kekalkan semua vektor yang dipulangkan dan simpan usage bersama operasi. Penggunaan identiti model yang tepat untuk pembenaman tersimpan menjelaskan koleksi dan perwakilan pertanyaan yang saling sepadan.", "alias_of": null, "aliases": [], "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00016000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000016", "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": "Maklumat lengkap tentang harga konfigurasi terdapat dalam butiran." } ], "ctx": null, "formats": [ "embeddings" ], "id": "Baichuan-Text-Embedding", "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": "Medan wajib, pemilih model, jenis atau bentuk input tidak sah.", "http_status": 400, "recovery": "Semak butiran pengesahan yang dikembalikan sebelum menyediakan permintaan berasingan.", "type": "invalid_request_error" }, { "condition": "Permintaan atau hasil asal tidak diketahui atau memerlukan semakan.", "http_status": null, "recovery": "Simpan pengecam dan pengepala asal; baca hanya rekod status/pustaka yang disokong. Jangan hantar semula permintaan penciptaan.", "type": null } ], "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/embeddings" }, "mode": "synchronous_json", "status": [], "unknown": "Simpan X-Client-Request-ID asal, respons dan pengepala. Hasil yang tidak diketahui atau result_pending_check tidak membenarkan penciptaan kedua.", "unknown_no_replay": true }, "outputs": { "all_results": "Simpan respons dan pengepala lengkap, pengecam asal, setiap output sebenar dan semua medan penggunaan/perakaunan. Simpan setiap aset unik milik anda dan kekalkan setiap peranan yang merujuk kepadanya.", "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": "Gunakan pemilih penghalaan Ailoot ini tepat seperti yang dinyatakan.", "default_known": false, "enum": [ "Baichuan-Text-Embedding" ], "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": [ "Baichuan-Text-Embedding" ], "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": "Baichuan-Text-Embedding" }, "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": [ "Gunakan pemilih penghalaan Ailoot ini tepat seperti yang dinyatakan." ], "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": "0.00016000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000016", "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\": \"Baichuan-Text-Embedding\", \"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": 0.16, "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": "0.00016000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000016", "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": "Baichuan-Text-Embedding", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00016000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000016", "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": "Baichuan-Text-Embedding", "request_model_id": "Baichuan-Text-Embedding", "route_label": "Ultra" } ], "name": "Baichuan-Text-Embedding", "price": { "in": 0.16, "in_label": null, "out": 0, "out_label": null, "scope_label": "Token rates", "sort_in": 0.16, "sort_out": 0, "varies": false }, "short": "Baichuan-Text-Embedding memulangkan vektor berangka bagi teks yang diberikan melalui API pembenaman Ailoot. Hantar satu petikan atau senarai dan simpan indeks respons semasa membina koleksi yang boleh dicari.", "title": "Baichuan-Text-Embedding" }, "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": "Baichuan-Text-Embedding memulangkan vektor berangka bagi teks yang diberikan melalui API pembenaman Ailoot. Hantar satu petikan atau senarai dan simpan indeks respons semasa membina koleksi yang boleh dicari.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "45fdc8af455c3ecbdc03553e69d931e475a41824389fa8f9b3ffdcd5deaabdb1", "renderer_sha256": "1dab5ba62521e9568c8b7c82005622cabbf330a075df7cce1f83274a04009460" }, "sourceRevision": "354e2a79b416145b63378939f64e8bd441b138fbecbfdc1e44f7dff8f84b6410" }