Gemini 3.1 Pro Preview murakkab material va bir nechta ketma-ket qaror talab qiladigan vazifalar bilan ishlash uchun mo‘ljallangan. Google dasturiy muhandislik, vositalardan foydalanish va faktlarga oid javoblarning izchilligini ta’kidlaydi. Model katta ma’lumotlar to‘plamlari, butun dasturiy repozitoriylar va matn vizual yoki ovozli materiallar bilan bog‘langan vazifalarni tahlil qilishga yordam beradi.
Google xususiyatlarida matn, tasvirlar, video, audio va PDF sanab o‘tilgan. Ailoot API’ning joriy katalogida matnli metodlar uchun kirishda matn va tasvirlar, chiqishda esa matn e’lon qilingan. Video, audio va PDF bu kirishlar to‘plamiga kiritilmagan. Uzun kontekst 1 048 576 tagacha kirish tokenini, matnli javob esa 65 536 tagacha chiqish tokenini sig‘diradi. Tasvir va audioni tushunish ularni yaratishni anglatmaydi: bu versiyaning chiqishi matnli bo‘lib qoladi.
Thinking ichki mulohazaga qo‘shimcha sa’y-harakat ajratishga imkon beradi. Google tavsiflagan bazaviy model rejimida standart daraja — high; low, medium, high mavjud. Bu qiymatlar Google’ning bazaviy modelini tavsiflaydi; Ailoot API orqali boshqarish rejimi va mavjudligi tanlangan metod hamda model variantiga bog‘liq. Asosiy ssenariylar murakkab dasturlash, algoritmlar yaratish, strategik rejalashtirish va yechimni qadamma-qadam yaxshilashni o‘z ichiga oladi. Bu, ayniqsa, bir nechta o‘zaro bog‘liq talabni hisobga olish va oraliq natijalarga qaytish kerak bo‘lganda foydali.
Model imkoniyatlari funksiya chaqiruvlari, kod bajarish va tuzilmali javoblarni o‘z ichiga oladi. Kontekstni keshlash, qidiruv natijalari bilan asoslash, Google Maps va veb-manzillar mazmunidan foydalanish qo‘llab-quvvatlanadi; paketli qayta ishlash ko‘zda tutilgan. Bunday to‘plam model ma’lumotlarni tahlil qilib, keyingi vositani tanlaydigan va yakuniy natijani shakllantiradigan jarayonni qurishga imkon beradi. Har bir vositaning haqiqiy mavjudligi model bilan ishlash metodiga bog‘liq.
Karta gemini-3.1-pro-preview dastlabki versiyasiga tegishli. Uning asosida Gemini 3 Pro turadi. Ishlab chiqaruvchi buyruq qobig‘i va foydalanuvchi vositalari bilan bog‘liq jarayonlarga yo‘naltirilgan gemini-3.1-pro-preview-customtools variantini alohida belgilaydi. Uning vosita tanlashdagi ustuvorliklari va xatti-harakat xususiyatlari oddiy Preview versiyasining xususiyatlari emas.
Modelda bazaviy yo‘nalishning umumiy cheklovlari saqlanadi: javoblarda to‘qima ma’lumotlar bo‘lishi, ayrim so‘rovlar esa sekin bajarilishi yoki kutish vaqti tugashi mumkin. Ko‘p bosqichli mulohaza, katta kontekst va ma’lumotlardan foydalanish murakkab topshiriq bilan ishlashga yordam beradi, lekin har bir yaratilgan xulosani tekshirilgan faktga aylantirmaydi.
", "aboutText": "Gemini 3.1 Pro Preview murakkab material va bir nechta ketma-ket qaror talab qiladigan vazifalar bilan ishlash uchun mo‘ljallangan. Google dasturiy muhandislik, vositalardan foydalanish va faktlarga oid javoblarning izchilligini ta’kidlaydi. Model katta ma’lumotlar to‘plamlari, butun dasturiy repozitoriylar va matn vizual yoki ovozli materiallar bilan bog‘langan vazifalarni tahlil qilishga yordam beradi.\n\n### Model qanday materiallarni tushunadi\n\nGoogle xususiyatlarida **matn, tasvirlar, video, audio va PDF** sanab o‘tilgan. Ailoot API’ning joriy katalogida matnli metodlar uchun kirishda **matn va tasvirlar**, chiqishda esa matn e’lon qilingan. Video, audio va PDF bu kirishlar to‘plamiga kiritilmagan. Uzun kontekst **1 048 576 tagacha kirish tokeni**ni, matnli javob esa **65 536 tagacha chiqish tokeni**ni sig‘diradi. Tasvir va audioni tushunish ularni yaratishni anglatmaydi: bu versiyaning chiqishi matnli bo‘lib qoladi.\n\n### Mulohaza yuritish va qadamma-qadam ishlash\n\nThinking ichki mulohazaga qo‘shimcha sa’y-harakat ajratishga imkon beradi. Google tavsiflagan bazaviy model rejimida standart daraja — `high`; `low`, `medium`, `high` mavjud. Bu qiymatlar Google’ning bazaviy modelini tavsiflaydi; Ailoot API orqali boshqarish rejimi va mavjudligi tanlangan metod hamda model variantiga bog‘liq. Asosiy ssenariylar murakkab dasturlash, algoritmlar yaratish, strategik rejalashtirish va yechimni qadamma-qadam yaxshilashni o‘z ichiga oladi. Bu, ayniqsa, bir nechta o‘zaro bog‘liq talabni hisobga olish va oraliq natijalarga qaytish kerak bo‘lganda foydali.\n\nModel imkoniyatlari funksiya chaqiruvlari, kod bajarish va tuzilmali javoblarni o‘z ichiga oladi. Kontekstni keshlash, qidiruv natijalari bilan asoslash, Google Maps va veb-manzillar mazmunidan foydalanish qo‘llab-quvvatlanadi; paketli qayta ishlash ko‘zda tutilgan. Bunday to‘plam model ma’lumotlarni tahlil qilib, keyingi vositani tanlaydigan va yakuniy natijani shakllantiradigan jarayonni qurishga imkon beradi. Har bir vositaning haqiqiy mavjudligi model bilan ishlash metodiga bog‘liq.\n\n### Versiya va xususiyatlar\n\nKarta `gemini-3.1-pro-preview` dastlabki versiyasiga tegishli. Uning asosida Gemini 3 Pro turadi. Ishlab chiqaruvchi buyruq qobig‘i va foydalanuvchi vositalari bilan bog‘liq jarayonlarga yo‘naltirilgan `gemini-3.1-pro-preview-customtools` variantini alohida belgilaydi. Uning vosita tanlashdagi ustuvorliklari va xatti-harakat xususiyatlari oddiy Preview versiyasining xususiyatlari emas.\n\nModelda bazaviy yo‘nalishning umumiy cheklovlari saqlanadi: javoblarda to‘qima ma’lumotlar bo‘lishi, ayrim so‘rovlar esa sekin bajarilishi yoki kutish vaqti tugashi mumkin. Ko‘p bosqichli mulohaza, katta kontekst va ma’lumotlardan foydalanish murakkab topshiriq bilan ishlashga yordam beradi, lekin har bir yaratilgan xulosani tekshirilgan faktga aylantirmaydi.", "editorialOverlay": { "copy_fields": { "practical_html": "Ingliz tilidagi texnik misol
Design a dependency-aware work order\n\nPlan execution for these tasks: A has no dependency; B depends on A; C depends on A; D depends on both B and C. At most two tasks may run together, and each takes one hour. Give the earliest valid schedule, a general scheduling algorithm and a test that catches starting D before C finishes.A time-slot schedule, algorithm pseudocode and a dependency-violation test case.Check that the proposed schedule respects both dependencies and the two-task concurrency limit.gemini-pro-agentMurakkab dasturlash vazifalari, katta materiallar tahlili va vositalar bilan aniq ko‘p bosqichli ishlash uchun Google’ning multimodal modeli.
Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.
Gemini 3.1 Pro Preview murakkab material va bir nechta ketma-ket qaror talab qiladigan vazifalar bilan ishlash uchun mo‘ljallangan. Google dasturiy muhandislik, vositalardan foydalanish va faktlarga oid javoblarning izchilligini ta’kidlaydi. Model katta ma’lumotlar to‘plamlari, butun dasturiy repozitoriylar va matn vizual yoki ovozli materiallar bilan bog‘langan vazifalarni tahlil qilishga yordam beradi.
Google xususiyatlarida matn, tasvirlar, video, audio va PDF sanab o‘tilgan. Ailoot API’ning joriy katalogida matnli metodlar uchun kirishda matn va tasvirlar, chiqishda esa matn e’lon qilingan. Video, audio va PDF bu kirishlar to‘plamiga kiritilmagan. Uzun kontekst 1 048 576 tagacha kirish tokenini, matnli javob esa 65 536 tagacha chiqish tokenini sig‘diradi. Tasvir va audioni tushunish ularni yaratishni anglatmaydi: bu versiyaning chiqishi matnli bo‘lib qoladi.
Thinking ichki mulohazaga qo‘shimcha sa’y-harakat ajratishga imkon beradi. Google tavsiflagan bazaviy model rejimida standart daraja — high; low, medium, high mavjud. Bu qiymatlar Google’ning bazaviy modelini tavsiflaydi; Ailoot API orqali boshqarish rejimi va mavjudligi tanlangan metod hamda model variantiga bog‘liq. Asosiy ssenariylar murakkab dasturlash, algoritmlar yaratish, strategik rejalashtirish va yechimni qadamma-qadam yaxshilashni o‘z ichiga oladi. Bu, ayniqsa, bir nechta o‘zaro bog‘liq talabni hisobga olish va oraliq natijalarga qaytish kerak bo‘lganda foydali.
Model imkoniyatlari funksiya chaqiruvlari, kod bajarish va tuzilmali javoblarni o‘z ichiga oladi. Kontekstni keshlash, qidiruv natijalari bilan asoslash, Google Maps va veb-manzillar mazmunidan foydalanish qo‘llab-quvvatlanadi; paketli qayta ishlash ko‘zda tutilgan. Bunday to‘plam model ma’lumotlarni tahlil qilib, keyingi vositani tanlaydigan va yakuniy natijani shakllantiradigan jarayonni qurishga imkon beradi. Har bir vositaning haqiqiy mavjudligi model bilan ishlash metodiga bog‘liq.
Karta gemini-3.1-pro-preview dastlabki versiyasiga tegishli. Uning asosida Gemini 3 Pro turadi. Ishlab chiqaruvchi buyruq qobig‘i va foydalanuvchi vositalari bilan bog‘liq jarayonlarga yo‘naltirilgan gemini-3.1-pro-preview-customtools variantini alohida belgilaydi. Uning vosita tanlashdagi ustuvorliklari va xatti-harakat xususiyatlari oddiy Preview versiyasining xususiyatlari emas.
Modelda bazaviy yo‘nalishning umumiy cheklovlari saqlanadi: javoblarda to‘qima ma’lumotlar bo‘lishi, ayrim so‘rovlar esa sekin bajarilishi yoki kutish vaqti tugashi mumkin. Ko‘p bosqichli mulohaza, katta kontekst va ma’lumotlardan foydalanish murakkab topshiriq bilan ishlashga yordam beradi, lekin har bir yaratilgan xulosani tekshirilgan faktga aylantirmaydi.
POST /v1/messagestext, imagetext| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
|---|---|---|---|---|
Maydonmodel | Turstring | TalabMajburiy | Qiymatlar va cheklovlar gemini-pro-agent | Standart— |
Maydonmessages | Turmessage[] | TalabMajburiy | Qiymatlar va cheklovlar Elementlarning eng kam soni: 1 | Standart— |
Maydonmessages[].role | Turstring | TalabMajburiy | Qiymatlar va cheklovlar user, assistant | Standart— |
Maydonmessages[].content | Turstring|content_block[] | TalabMajburiy | Qiymatlar va cheklovlar | Standart— |
Maydonsystem | Turstring|content_block[] | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydonmax_tokens | Turinteger | TalabMajburiy | Qiymatlar va cheklovlar Eng kam: 1 Cheklov qo‘llanadigan obyekt: Tanlangan model yoki kartaning aniq yuqori chegarasi | Standart— |
Maydonstream | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standartfalse |
Maydontemperature | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontop_p | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonstop_sequences | Turstring[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools | Turtool[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools[].name | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Quyidagi holatda qo‘llanadi: Vosita taqdim etilgan | Standart— |
Maydontools[].description | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydontools[].input_schema | TurJSON_schema | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydontool_choice | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonthinking.type | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonthinking.budget_tokens | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonoutput_config.effort | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydonmetadata | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Maydoncache_control | Turobject | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Matn · UTF8 matni
messages[].content yoki type=text bloki
Matn + rasm
{type:image,source:{type:base64,media_type,data}} yoki type=url turidagi manba
| Parametr | Parametr talablari |
|---|---|
Parametrmaterial_types | Parametr talablaritext, image |
Parametrrequired | Parametr talablarimodel, messages, max_tokens |
Parametroptional_controls | Parametr talablarinull |
Parametrcontent_type | Parametr talablariapplication/json |
Parametrtext_file_material | Parametr talablariHaqiqiy UTF-8 kontenti matn sifatida kiritiladi; fayl IDlari/URLs uchun alohida aniq qo‘llab-quvvatlash kerak |
Hayot sikli: exact retained published method
Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida.
POST /v1/messages| So‘rov hajmi, tokenlarda | Kirish | Chiqish | Keshdan o‘qish | Keshga yozish | Kesh 5 daqiqa | Kesh 1 soat |
|---|---|---|---|---|---|---|
| So‘rov hajmi, tokenlarda(0; 200 000] | Kirish0,002 US$ | Chiqish0,012 US$ | Keshdan o‘qish0,0002 US$ | Keshga yozishAsosiy: — | Kesh 5 daqiqaAsosiy: — | Kesh 1 soatAsosiy: — |
| So‘rov hajmi, tokenlarda(200 000; ∞) | Kirish0,004 US$ | Chiqish0,018 US$ | Keshdan o‘qish0,0004 US$ | Keshga yozishAsosiy: — | Kesh 5 daqiqaAsosiy: — | Kesh 1 soatAsosiy: — |
1K token uchun USD tariflari. Tarif belgilanmagan bo‘lsa, ushbu usulning asosiy tarifi qo‘llanadi; «—» asosiy tarif belgilanmaganini bildiradi.
Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gemini-pro-agent", "max_tokens": 1024, "messages": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\\n\\n"+text}]\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/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"\nexport AILOOT_IMAGE_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gemini-pro-agent", "max_tokens": 1024, "messages": [{"role": "user", "content": [{"type": "text", "text": "Read labels and numbers in this image; flag unreadable text."}]}]}')\nimport base64\nf=Path(os.environ["AILOOT_IMAGE_FILE"])\nif not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")\nraw=f.read_bytes()\nmime="image/png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "image/jpeg" if raw.startswith(b"\\xff\\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None\nif not mime:raise SystemExit("Use PNG JPEG or WebP")\nb64=base64.b64encode(raw).decode()\np["messages"][0]["content"].append({"type":"image","source":{"type":"base64","media_type":mime,"data":b64}})\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/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -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\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nimport anthropic\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = anthropic.Anthropic(base_url="https://api.ailoot.ai", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nmsg = client.messages.create(\n model="gemini-pro-agent",\n max_tokens=1024,\n messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],\n)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(msg.to_dict(mode="json"))\nimport Anthropic from "@anthropic-ai/sdk";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new Anthropic({ baseURL: "https://api.ailoot.ai", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst msg = await client.messages.create({\n model: "gemini-pro-agent",\n max_tokens: 1024,\n messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],\n});\nimport { writeFileSync } from "node:fs";\n\nfunction saveResult(data, stem = "result") {\n writeFileSync(stem + ".json", JSON.stringify(data, null, 2));\n console.log(JSON.stringify(data, null, 2));\n const saveBlob = (encoded, suffix, label) => {\n const raw = Buffer.from(encoded, "base64");\n const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;\n const file = `${stem}-${label}.${ext}`;\n writeFileSync(file, raw); console.log("Saved", file);\n };\n for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));\n for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));\n for (const [i, item] of (data.output || []).entries()) {\n console.log("Output", i, JSON.stringify(item));\n if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);\n }\n for (const [ci, candidate] of (data.candidates || []).entries()) {\n for (const [pi, part] of (candidate.content?.parts || []).entries()) {\n console.log("Candidate/part", ci, pi, JSON.stringify(part));\n const blob = part.inlineData || part.inline_data;\n if (blob?.data) {\n const mime = blob.mimeType || blob.mime_type;\n const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";\n saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);\n }\n }\n }\n}\n\nsaveResult(msg);\nimport anthropic,os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=anthropic.Anthropic(base_url="https://api.ailoot.ai",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.messages.stream(model='gemini-pro-agent',max_tokens=1024,messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_message()\n save_result(final.to_dict(mode="json"),"stream-result")\njson yoki SSE
Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.
Barcha kontent bloklari, fikrlash imzolari va haqiqiy vosita ishlatish IDlarini saqlang. SSE bloklarining barcha o‘zgarish qismlarini yig‘ing; birinchi matn bloki to‘liq chiqish emas.
So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.
| Maydon | Tur |
|---|---|
Maydonid | Turstring |
Maydonmodel | Turstring |
Maydoncontent[] | Turtext|thinking|tool_use|other content block |
Maydonstop_reason | Turstring|null |
Maydonusage | Turobject |
| Javob | Shart | Keyingi qadam |
|---|---|---|
Javob400 · invalid_request_error | ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri | Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating. |
Ailoot gemini-pro-agent
Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.
gemini-pro-agentMurakkab dasturlash vazifalari, katta materiallar tahlili va vositalar bilan aniq ko‘p bosqichli ishlash uchun Google’ning multimodal modeli.
Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.
Gemini 3.1 Pro Preview murakkab material va bir nechta ketma-ket qaror talab qiladigan vazifalar bilan ishlash uchun mo‘ljallangan. Google dasturiy muhandislik, vositalardan foydalanish va faktlarga oid javoblarning izchilligini ta’kidlaydi. Model katta ma’lumotlar to‘plamlari, butun dasturiy repozitoriylar va matn vizual yoki ovozli materiallar bilan bog‘langan vazifalarni tahlil qilishga yordam beradi.
Google xususiyatlarida matn, tasvirlar, video, audio va PDF sanab o‘tilgan. Ailoot API’ning joriy katalogida matnli metodlar uchun kirishda matn va tasvirlar, chiqishda esa matn e’lon qilingan. Video, audio va PDF bu kirishlar to‘plamiga kiritilmagan. Uzun kontekst 1 048 576 tagacha kirish tokenini, matnli javob esa 65 536 tagacha chiqish tokenini sig‘diradi. Tasvir va audioni tushunish ularni yaratishni anglatmaydi: bu versiyaning chiqishi matnli bo‘lib qoladi.
Thinking ichki mulohazaga qo‘shimcha sa’y-harakat ajratishga imkon beradi. Google tavsiflagan bazaviy model rejimida standart daraja — high; low, medium, high mavjud. Bu qiymatlar Google’ning bazaviy modelini tavsiflaydi; Ailoot API orqali boshqarish rejimi va mavjudligi tanlangan metod hamda model variantiga bog‘liq. Asosiy ssenariylar murakkab dasturlash, algoritmlar yaratish, strategik rejalashtirish va yechimni qadamma-qadam yaxshilashni o‘z ichiga oladi. Bu, ayniqsa, bir nechta o‘zaro bog‘liq talabni hisobga olish va oraliq natijalarga qaytish kerak bo‘lganda foydali.
Model imkoniyatlari funksiya chaqiruvlari, kod bajarish va tuzilmali javoblarni o‘z ichiga oladi. Kontekstni keshlash, qidiruv natijalari bilan asoslash, Google Maps va veb-manzillar mazmunidan foydalanish qo‘llab-quvvatlanadi; paketli qayta ishlash ko‘zda tutilgan. Bunday to‘plam model ma’lumotlarni tahlil qilib, keyingi vositani tanlaydigan va yakuniy natijani shakllantiradigan jarayonni qurishga imkon beradi. Har bir vositaning haqiqiy mavjudligi model bilan ishlash metodiga bog‘liq.
Karta gemini-3.1-pro-preview dastlabki versiyasiga tegishli. Uning asosida Gemini 3 Pro turadi. Ishlab chiqaruvchi buyruq qobig‘i va foydalanuvchi vositalari bilan bog‘liq jarayonlarga yo‘naltirilgan gemini-3.1-pro-preview-customtools variantini alohida belgilaydi. Uning vosita tanlashdagi ustuvorliklari va xatti-harakat xususiyatlari oddiy Preview versiyasining xususiyatlari emas.
Modelda bazaviy yo‘nalishning umumiy cheklovlari saqlanadi: javoblarda to‘qima ma’lumotlar bo‘lishi, ayrim so‘rovlar esa sekin bajarilishi yoki kutish vaqti tugashi mumkin. Ko‘p bosqichli mulohaza, katta kontekst va ma’lumotlardan foydalanish murakkab topshiriq bilan ishlashga yordam beradi, lekin har bir yaratilgan xulosani tekshirilgan faktga aylantirmaydi.
POST /v1beta/models/{model}:generateContenttext, imagetext| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
|---|---|---|---|---|
Maydonpath.model | Turstring | TalabMajburiy | Qiymatlar va cheklovlar gemini-pro-agent | Standart— |
Maydoncontents | Turcontent[] | TalabMajburiy | Qiymatlar va cheklovlar Elementlarning eng kam soni: 1 | Standart— |
Maydoncontents[].role | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar user, model | Standart— |
Maydoncontents[].parts | Turpart[] | TalabMajburiy | Qiymatlar va cheklovlar | Standart— |
MaydonsystemInstruction | Turcontent | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
MaydongenerationConfig.maxOutputTokens | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 Modelga bogʻliq | Standart— |
MaydongenerationConfig.temperature | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
MaydongenerationConfig.topP | Turnumber | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
MaydongenerationConfig.topK | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
MaydongenerationConfig.stopSequences | Turstring[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
MaydongenerationConfig.thinkingConfig.includeThoughts | Turboolean | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
MaydongenerationConfig.thinkingConfig.thinkingBudget | Turinteger | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
Maydontools[].functionDeclarations | Turfunction_declaration[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
MaydontoolConfig.functionCallingConfig.mode | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
MaydonsafetySettings | Turobject[] | TalabIxtiyoriy | Qiymatlar va cheklovlar Modelga bogʻliq | Standart— |
MaydonsessionId | Turstring | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |
Matn · UTF8 matni
contents[].parts[].text
Matn + rasm
parts[].inlineData={mimeType,data}
| Parametr | Parametr talablari |
|---|---|
Parametrmaterial_types | Parametr talablaritext, image |
Parametrrequired | Parametr talablaricontents |
Parametroptional_controls | Parametr talablarinull |
Parametrcontent_type | Parametr talablariapplication/json |
Parametrtext_file_material | Parametr talablariHaqiqiy UTF-8 kontenti matn sifatida kiritiladi; fayl IDlari/URLs uchun alohida aniq qo‘llab-quvvatlash kerak |
Hayot sikli: exact retained published method
Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida.
POST /v1beta/models/{model}:generateContent| So‘rov hajmi, tokenlarda | Kirish | Chiqish | Keshdan o‘qish | Keshga yozish | Kesh 5 daqiqa | Kesh 1 soat |
|---|---|---|---|---|---|---|
| So‘rov hajmi, tokenlarda(0; 200 000] | Kirish0,002 US$ | Chiqish0,012 US$ | Keshdan o‘qish0,0002 US$ | Keshga yozishAsosiy: — | Kesh 5 daqiqaAsosiy: — | Kesh 1 soatAsosiy: — |
| So‘rov hajmi, tokenlarda(200 000; ∞) | Kirish0,004 US$ | Chiqish0,018 US$ | Keshdan o‘qish0,0004 US$ | Keshga yozishAsosiy: — | Kesh 5 daqiqaAsosiy: — | Kesh 1 soatAsosiy: — |
1K token uchun USD tariflari. Tarif belgilanmagan bo‘lsa, ushbu usulning asosiy tarifi qo‘llanadi; «—» asosiy tarif belgilanmaganini bildiradi.
Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"contents": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["contents"]=[{"role":"user","parts":[{"text":"Summarize this material and quote supporting source passages.\\n\\n"+text}]}]\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1beta/models/gemini-pro-agent:generateContent \\\n -H "x-goog-api-key: $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(json.loads(Path("result.json").read_text(encoding="utf-8")))\nPY_RESULT\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"\nexport AILOOT_IMAGE_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"contents": [{"role": "user", "parts": [{"text": "Read labels and numbers in this image; flag unreadable text."}]}]}')\nimport base64\nf=Path(os.environ["AILOOT_IMAGE_FILE"])\nif not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")\nraw=f.read_bytes()\nmime="image/png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "image/jpeg" if raw.startswith(b"\\xff\\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None\nif not mime:raise SystemExit("Use PNG JPEG or WebP")\nb64=base64.b64encode(raw).decode()\np["contents"][0]["parts"].append({"inlineData":{"mimeType":mime,"data":b64}})\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1beta/models/gemini-pro-agent:generateContent \\\n -H "x-goog-api-key: $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(json.loads(Path("result.json").read_text(encoding="utf-8")))\nPY_RESULT\nfrom google import genai\nfrom google.genai import types\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = genai.Client(\n api_key=os.environ["AILOOT_API_KEY"],\n http_options=types.HttpOptions(base_url="https://api.ailoot.ai"),\n)\nresp = client.models.generate_content(model="gemini-pro-agent", contents="Explain the difference between an observation and a hypothesis in three sentences.")\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(resp.to_json_dict())\nimport { GoogleGenAI } from "@google/genai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst ai = new GoogleGenAI({ apiKey: process.env.AILOOT_API_KEY, httpOptions: { baseUrl: "https://api.ailoot.ai" } });\nconst resp = await ai.models.generateContent({ model: "gemini-pro-agent", contents: "Explain the difference between an observation and a hypothesis in three sentences." });\nimport { writeFileSync } from "node:fs";\n\nfunction saveResult(data, stem = "result") {\n writeFileSync(stem + ".json", JSON.stringify(data, null, 2));\n console.log(JSON.stringify(data, null, 2));\n const saveBlob = (encoded, suffix, label) => {\n const raw = Buffer.from(encoded, "base64");\n const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;\n const file = `${stem}-${label}.${ext}`;\n writeFileSync(file, raw); console.log("Saved", file);\n };\n for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));\n for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));\n for (const [i, item] of (data.output || []).entries()) {\n console.log("Output", i, JSON.stringify(item));\n if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);\n }\n for (const [ci, candidate] of (data.candidates || []).entries()) {\n for (const [pi, part] of (candidate.content?.parts || []).entries()) {\n console.log("Candidate/part", ci, pi, JSON.stringify(part));\n const blob = part.inlineData || part.inline_data;\n if (blob?.data) {\n const mime = blob.mimeType || blob.mime_type;\n const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";\n saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);\n }\n }\n }\n}\n\nsaveResult(resp);\nfrom google import genai\nfrom google.genai import types\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=genai.Client(api_key=os.environ["AILOOT_API_KEY"],http_options=types.HttpOptions(base_url="https://api.ailoot.ai"))\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\ncount=0\nwith Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for chunk in client.models.generate_content_stream(model='gemini-pro-agent',contents="Explain observations and hypotheses in three sentences."):\n payload=chunk.to_json_dict()\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n save_result(payload,"stream-chunk-"+str(count))\n count+=1\njson yoki qo‘llab-quvvatlanadigan oqimli uzatish
Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.
Funksiya chaqiruvlari, fikrlash imzolari va sarf ma’lumotlari bilan birga barcha nomzod javoblar va qismlarni saqlang; oqim qismlarini tartib bilan yig‘ing.
So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.
| Maydon | Tur |
|---|---|
MaydonresponseId | Turstring |
MaydonmodelVersion | Turstring |
Maydoncandidates[].content.parts[] | Turtext|inlineData|functionCall|other part |
Maydoncandidates[].finishReason | Turstring |
MaydonusageMetadata | Turobject |
| Javob | Shart | Keyingi qadam |
|---|---|---|
Javob400 · invalid_request_error | ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri | Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating. |
Ailoot gemini-pro-agent
Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.
Ingliz tilidagi texnik misol
Design a dependency-aware work order\n\nPlan execution for these tasks: A has no dependency; B depends on A; C depends on A; D depends on both B and C. At most two tasks may run together, and each takes one hour. Give the earliest valid schedule, a general scheduling algorithm and a test that catches starting D before C finishes.A time-slot schedule, algorithm pseudocode and a dependency-violation test case.Check that the proposed schedule respects both dependencies and the two-task concurrency limit.