Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
", "aboutText": "Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan. Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens. Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.", "editorialOverlay": { "copy_fields": { "about_html": "Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
", "practical_html": "Contoh teknikal dalam bahasa Inggeris
Diagnose duplicated webhook effects\n\nReview this flow: receive event; charge wallet; save event_id. If saving fails after charging, redelivery charges again. Propose a corrected transaction boundary using a unique event_id record and the wallet update. State the expected behavior for first delivery, duplicate delivery and failure before commit. Return pseudocode and checks; do not claim to execute them.Transaction pseudocode and a three-case behavior table for first, duplicate and rolled-back delivery.The deduplication record and wallet effect commit together, and a duplicate produces no second debit.claude-sonnet-5Claude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/messagestext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet-5 | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had user, assistant | Lalai— |
Medanmessages[].content | Jenisstring|content_block[] | KeperluanDiperlukan | Nilai & had | Lalai— |
Medansystem | Jenisstring|content_block[] | KeperluanPilihan | Nilai & had | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanDiperlukan | Nilai & had Minimum: 1 Had terpakai pada: had maksimum tepat bagi model/kad yang dipilih | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstop_sequences | Jenisstring[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenistool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].name | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: alat dibekalkan | Lalai— |
Medantools[].description | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medantools[].input_schema | JenisJSON_schema | KeperluanPilihan | Nilai & had | Lalai— |
Medantool_choice | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanthinking.type | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanthinking.budget_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanoutput_config.effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanmetadata | Jenisobject | KeperluanPilihan | Nilai & had | Lalai— |
Medancache_control | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
messages[].content atau blok type=text
Teks + imej
{type:image,source:{type:base64,media_type,data}} atau sumber dengan type=url
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, messages, max_tokens |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/messagesAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet-5", "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": "claude-sonnet-5", "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="claude-sonnet-5",\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: "claude-sonnet-5",\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='claude-sonnet-5',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 atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanid | Jenisstring |
Medanmodel | Jenisstring |
Medancontent[] | Jenistext|thinking|tool_use|other content block |
Medanstop_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnet-5Claude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/responsestext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet-5 | Lalai— |
Medaninput | Jenisstring|input_item[] | KeperluanDiperlukan | Nilai & had | Lalai— |
Medaninstructions | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medaninput[].type | Jenisstring | KeperluanPilihan | Nilai & had message, reasoning, function_call, function_call_output | Lalai— |
Medaninput[].content | Jenisstring|content_part[] | KeperluanPilihan | Nilai & had | Lalai— |
Medaninput[].call_id | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: function_call_output; kekalkan call_id sebenar yang dipulangkan | Lalai— |
Medaninput[].output | Jenisstring|content_part[] | KeperluanPilihan | Nilai & had Terpakai apabila: function_call_output | Lalai— |
Medanmax_output_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenistool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantool_choice | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanparallel_tool_calls | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning.effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning.summary | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantext.format | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantext.verbosity | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medaninclude | Jenisstring[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstore | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprevious_response_id | Jenisstring | KeperluanPilihan | Nilai & had Keperluan ID: Hanya ID respons sebenar daripada penerusan yang disokong Bergantung pada model | Lalai— |
Medanservice_tier | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_key | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_options | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
Teks input atau bahagian input_text
Teks + imej
{type:input_image,image_url}
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, input |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/responsesAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet-5", "input": ""}')\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["input"]="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/responses \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport 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('{"model": "claude-sonnet-5", "input": [{"role": "user", "content": [{"type": "input_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["input"][0]["content"].append({"type":"input_image","image_url":"data:"+mime+";base64,"+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/responses \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport 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 openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nresp = client.responses.create(model="claude-sonnet-5", input="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_dict(mode="json"))\nimport OpenAI from "openai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst resp = await client.responses.create({ model: "claude-sonnet-5", input: "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 openai import OpenAI\nimport os\nfrom pathlib import Path\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\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nwith client.responses.stream(model="claude-sonnet-5", input="Explain the difference between an observation and a hypothesis 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_response()\n save_result(final.to_dict(mode="json"),"stream-result")\njson atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan setiap item output dan bahagian kandungan, call_id fungsi sebenar, penaakulan disulitkan serta data penggunaan. Semasa penstriman, kekalkan identiti peristiwa/item yang lengkap.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanid | Jenisstring |
Medanstatus | Jenisstring |
Medanoutput[] | Jenismessage|reasoning|function_call|other item |
Medanoutput[].content[] | Jeniscontent part |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnet-5Claude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/chat/completionstext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet-5 | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had Contoh: system, user, assistant, tool, function Syarat: Peranan ini diwakili secara eksplisit oleh jenis keserasian dalam kod sumber; set peranan model yang tepat kekal khusus untuk setiap model. Bergantung pada model | Lalai— |
Medanmessages[].content | Jenisstring|content_part[]|null | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].tool_calls | Jenistool_call[] | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].tool_call_id | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: role=tool; gunakan ID sebenar yang dipulangkan | Lalai— |
Medanmessages[].name | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].reasoning_content | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: Hantar semula penaakulan sebenar yang dipulangkan apabila model ini menggunakannya | Lalai— |
Medanmessages[].reasoning_details | Jenisobject|array | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanmax_completion_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medanstream_options.include_usage | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstop | Jenisstring|string[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenisfunction_tool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].type | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].function.name | Jenisstring | KeperluanDiperlukan | Nilai & had Terpakai apabila: alat fungsi dibekalkan | Lalai— |
Medantools[].function.description | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medantools[].function.parameters | JenisJSON_schema | KeperluanPilihan | Nilai & had | Lalai— |
Medantool_choice | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanparallel_tool_calls | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanresponse_format | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning_effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanservice_tier | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medaninstructions | Jenisstring | KeperluanPilihan | Nilai & had Medan keserasian | Lalai— |
Medanfunctions | Jenisfunction[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanfunction_call | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_options | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
messages[].content
Teks + imej
{type:image_url,image_url:{url,detail}}
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, messages |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/chat/completionsAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet-5", "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/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nset -eu\n: "${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": "claude-sonnet-5", "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_url","image_url":{"url":"data:"+mime+";base64,"+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/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nresp = client.chat.completions.create(\n model="claude-sonnet-5",\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(resp.to_dict(mode="json"))\nimport OpenAI from "openai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst resp = await client.chat.completions.create({\n model: "claude-sonnet-5",\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(resp);\nfrom openai import OpenAI\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=OpenAI(base_url="https://api.ailoot.ai/v1",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.chat.completions.stream(model='claude-sonnet-5',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_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanchoices[].index | Jenisinteger |
Medanchoices[].message.content | Jenisstring|null |
Medanchoices[].message.tool_calls | Jenistool_call[] |
Medanchoices[].message.reasoning_content | Jenisstring |
Medanchoices[].message.reasoning_details | Jenisobject|array |
Medanchoices[].finish_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnet-5Claude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/chat/completionstexttext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet-5 | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had Bergantung pada model | Lalai— |
Medanmessages[].content | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: Perbualan teks | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Teks · Teks UTF8
messages[].content
| Parameter | Kontrak |
|---|---|
Parametercontent_type | Kontrakapplication/json |
Parameterrequired | Kontrakmodel, messages |
Parametermaterial_types | Kontraktext |
Parametermessages | Kontraktatasusunan mesej dengan struktur role/content |
Parameterfile_material | Kontrakdokumen UTF-8 sebenar dimasukkan sebagai teks literal; ID fail tidak diandaikan |
Parameteroptional_controls | Kontrakstream |
Parametercontrol_scope | KontrakPerbualan teks; stream ialah nilai boolean dengan false sebagai lalai. |
Parameteroutput_modalities | Kontraktext |
Parameterhosted_operations | Kontrakfalse |
Parametermultimodal_input_binding | Kontraknull |
Alias kaedah: /chat/completions
Kitar hayat: synchronous_or_streamed
Hantar ID model yang tepat bersama messages.
Panjang konteks bukan had output.
Fail teks boleh membekalkan teks literal; input imej/audio/objek fail memerlukan pengikatan tepat.
Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiKadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiTahap panjang permintaan 1 | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model":"claude-sonnet-5","messages":[{"role":"user","content":"hello"}],"stream":false}')\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/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model="claude-sonnet-5", messages=[{"role":"user","content":"hello"}], stream=False)\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(result.to_dict(mode="json"))\njson atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanchoices[].index | Jenisinteger |
Medanchoices[].message.content | Jenisstring|null |
Medanchoices[].message.tool_calls | Jenistool_call[] |
Medanchoices[].message.reasoning_content | Jenisstring |
Medanchoices[].message.reasoning_details | Jenisobject|array |
Medanchoices[].finish_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnet-5Claude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/messagestextjson, sse| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet-5 | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had user, assistant | Lalai— |
Medanmessages[].content | Jenisstring | KeperluanDiperlukan | Nilai & had | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanDiperlukan | Nilai & had | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Teks · Teks UTF-8
rentetan messages[].content
| Parameter | Kontrak |
|---|---|
Parametercontent_type | Kontrakapplication/json |
Parameterrequired | Kontrakmodel, messages, max_tokens |
Parameteroptional_controls | Kontrakstream |
Parametermaterial_types | Kontraktext |
Parametertext_file_material | KontrakBaca dokumen UTF-8 sebenar dan letakkan teksnya dalam rentetan input atau kandungan mesej yang ditunjukkan. |
Kitar hayat: synchronous_or_streamed
Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiKadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiTahap panjang permintaan 1 | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →
{\n "model": "claude-sonnet-5",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'claude-sonnet-5', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'claude-sonnet-5', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanid | Jenisstring |
Medanmodel | Jenisstring |
Medancontent[] | Jenistext|thinking|tool_use|other content block |
Medanstop_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnetClaude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/messagestext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had user, assistant | Lalai— |
Medanmessages[].content | Jenisstring|content_block[] | KeperluanDiperlukan | Nilai & had | Lalai— |
Medansystem | Jenisstring|content_block[] | KeperluanPilihan | Nilai & had | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanDiperlukan | Nilai & had Minimum: 1 Had terpakai pada: had maksimum tepat bagi model/kad yang dipilih | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstop_sequences | Jenisstring[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenistool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].name | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: alat dibekalkan | Lalai— |
Medantools[].description | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medantools[].input_schema | JenisJSON_schema | KeperluanPilihan | Nilai & had | Lalai— |
Medantool_choice | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanthinking.type | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanthinking.budget_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanoutput_config.effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanmetadata | Jenisobject | KeperluanPilihan | Nilai & had | Lalai— |
Medancache_control | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
messages[].content atau blok type=text
Teks + imej
{type:image,source:{type:base64,media_type,data}} atau sumber dengan type=url
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, messages, max_tokens |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/messagesAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet", "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": "claude-sonnet", "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="claude-sonnet",\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: "claude-sonnet",\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='claude-sonnet',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 atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanid | Jenisstring |
Medanmodel | Jenisstring |
Medancontent[] | Jenistext|thinking|tool_use|other content block |
Medanstop_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnetClaude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/responsestext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet | Lalai— |
Medaninput | Jenisstring|input_item[] | KeperluanDiperlukan | Nilai & had | Lalai— |
Medaninstructions | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medaninput[].type | Jenisstring | KeperluanPilihan | Nilai & had message, reasoning, function_call, function_call_output | Lalai— |
Medaninput[].content | Jenisstring|content_part[] | KeperluanPilihan | Nilai & had | Lalai— |
Medaninput[].call_id | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: function_call_output; kekalkan call_id sebenar yang dipulangkan | Lalai— |
Medaninput[].output | Jenisstring|content_part[] | KeperluanPilihan | Nilai & had Terpakai apabila: function_call_output | Lalai— |
Medanmax_output_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenistool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantool_choice | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanparallel_tool_calls | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning.effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning.summary | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantext.format | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantext.verbosity | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medaninclude | Jenisstring[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstore | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprevious_response_id | Jenisstring | KeperluanPilihan | Nilai & had Keperluan ID: Hanya ID respons sebenar daripada penerusan yang disokong Bergantung pada model | Lalai— |
Medanservice_tier | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_key | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_options | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
Teks input atau bahagian input_text
Teks + imej
{type:input_image,image_url}
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, input |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/responsesAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet", "input": ""}')\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["input"]="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/responses \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport 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('{"model": "claude-sonnet", "input": [{"role": "user", "content": [{"type": "input_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["input"][0]["content"].append({"type":"input_image","image_url":"data:"+mime+";base64,"+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/responses \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport 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 openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nresp = client.responses.create(model="claude-sonnet", input="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_dict(mode="json"))\nimport OpenAI from "openai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst resp = await client.responses.create({ model: "claude-sonnet", input: "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 openai import OpenAI\nimport os\nfrom pathlib import Path\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\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nwith client.responses.stream(model="claude-sonnet", input="Explain the difference between an observation and a hypothesis 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_response()\n save_result(final.to_dict(mode="json"),"stream-result")\njson atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan setiap item output dan bahagian kandungan, call_id fungsi sebenar, penaakulan disulitkan serta data penggunaan. Semasa penstriman, kekalkan identiti peristiwa/item yang lengkap.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanid | Jenisstring |
Medanstatus | Jenisstring |
Medanoutput[] | Jenismessage|reasoning|function_call|other item |
Medanoutput[].content[] | Jeniscontent part |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnetClaude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/chat/completionstext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had Contoh: system, user, assistant, tool, function Syarat: Peranan ini diwakili secara eksplisit oleh jenis keserasian dalam kod sumber; set peranan model yang tepat kekal khusus untuk setiap model. Bergantung pada model | Lalai— |
Medanmessages[].content | Jenisstring|content_part[]|null | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].tool_calls | Jenistool_call[] | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].tool_call_id | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: role=tool; gunakan ID sebenar yang dipulangkan | Lalai— |
Medanmessages[].name | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].reasoning_content | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: Hantar semula penaakulan sebenar yang dipulangkan apabila model ini menggunakannya | Lalai— |
Medanmessages[].reasoning_details | Jenisobject|array | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanmax_completion_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medanstream_options.include_usage | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstop | Jenisstring|string[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenisfunction_tool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].type | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].function.name | Jenisstring | KeperluanDiperlukan | Nilai & had Terpakai apabila: alat fungsi dibekalkan | Lalai— |
Medantools[].function.description | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medantools[].function.parameters | JenisJSON_schema | KeperluanPilihan | Nilai & had | Lalai— |
Medantool_choice | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanparallel_tool_calls | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanresponse_format | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning_effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanservice_tier | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medaninstructions | Jenisstring | KeperluanPilihan | Nilai & had Medan keserasian | Lalai— |
Medanfunctions | Jenisfunction[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanfunction_call | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_options | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
messages[].content
Teks + imej
{type:image_url,image_url:{url,detail}}
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, messages |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/chat/completionsAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet", "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/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nset -eu\n: "${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": "claude-sonnet", "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_url","image_url":{"url":"data:"+mime+";base64,"+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/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nresp = client.chat.completions.create(\n model="claude-sonnet",\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(resp.to_dict(mode="json"))\nimport OpenAI from "openai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst resp = await client.chat.completions.create({\n model: "claude-sonnet",\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(resp);\nfrom openai import OpenAI\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=OpenAI(base_url="https://api.ailoot.ai/v1",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.chat.completions.stream(model='claude-sonnet',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_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanchoices[].index | Jenisinteger |
Medanchoices[].message.content | Jenisstring|null |
Medanchoices[].message.tool_calls | Jenistool_call[] |
Medanchoices[].message.reasoning_content | Jenisstring |
Medanchoices[].message.reasoning_details | Jenisobject|array |
Medanchoices[].finish_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnet-latestClaude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/messagestext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet-latest | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had user, assistant | Lalai— |
Medanmessages[].content | Jenisstring|content_block[] | KeperluanDiperlukan | Nilai & had | Lalai— |
Medansystem | Jenisstring|content_block[] | KeperluanPilihan | Nilai & had | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanDiperlukan | Nilai & had Minimum: 1 Had terpakai pada: had maksimum tepat bagi model/kad yang dipilih | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstop_sequences | Jenisstring[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenistool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].name | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: alat dibekalkan | Lalai— |
Medantools[].description | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medantools[].input_schema | JenisJSON_schema | KeperluanPilihan | Nilai & had | Lalai— |
Medantool_choice | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanthinking.type | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanthinking.budget_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanoutput_config.effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanmetadata | Jenisobject | KeperluanPilihan | Nilai & had | Lalai— |
Medancache_control | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
messages[].content atau blok type=text
Teks + imej
{type:image,source:{type:base64,media_type,data}} atau sumber dengan type=url
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, messages, max_tokens |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/messagesAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet-latest", "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": "claude-sonnet-latest", "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="claude-sonnet-latest",\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: "claude-sonnet-latest",\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='claude-sonnet-latest',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 atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanid | Jenisstring |
Medanmodel | Jenisstring |
Medancontent[] | Jenistext|thinking|tool_use|other content block |
Medanstop_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnet-latestClaude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/responsestext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet-latest | Lalai— |
Medaninput | Jenisstring|input_item[] | KeperluanDiperlukan | Nilai & had | Lalai— |
Medaninstructions | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medaninput[].type | Jenisstring | KeperluanPilihan | Nilai & had message, reasoning, function_call, function_call_output | Lalai— |
Medaninput[].content | Jenisstring|content_part[] | KeperluanPilihan | Nilai & had | Lalai— |
Medaninput[].call_id | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: function_call_output; kekalkan call_id sebenar yang dipulangkan | Lalai— |
Medaninput[].output | Jenisstring|content_part[] | KeperluanPilihan | Nilai & had Terpakai apabila: function_call_output | Lalai— |
Medanmax_output_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenistool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantool_choice | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanparallel_tool_calls | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning.effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning.summary | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantext.format | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantext.verbosity | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medaninclude | Jenisstring[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstore | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprevious_response_id | Jenisstring | KeperluanPilihan | Nilai & had Keperluan ID: Hanya ID respons sebenar daripada penerusan yang disokong Bergantung pada model | Lalai— |
Medanservice_tier | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_key | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_options | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
Teks input atau bahagian input_text
Teks + imej
{type:input_image,image_url}
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, input |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/responsesAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet-latest", "input": ""}')\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["input"]="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/responses \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport 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('{"model": "claude-sonnet-latest", "input": [{"role": "user", "content": [{"type": "input_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["input"][0]["content"].append({"type":"input_image","image_url":"data:"+mime+";base64,"+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/responses \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport 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 openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nresp = client.responses.create(model="claude-sonnet-latest", input="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_dict(mode="json"))\nimport OpenAI from "openai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst resp = await client.responses.create({ model: "claude-sonnet-latest", input: "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 openai import OpenAI\nimport os\nfrom pathlib import Path\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\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nwith client.responses.stream(model="claude-sonnet-latest", input="Explain the difference between an observation and a hypothesis 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_response()\n save_result(final.to_dict(mode="json"),"stream-result")\njson atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan setiap item output dan bahagian kandungan, call_id fungsi sebenar, penaakulan disulitkan serta data penggunaan. Semasa penstriman, kekalkan identiti peristiwa/item yang lengkap.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanid | Jenisstring |
Medanstatus | Jenisstring |
Medanoutput[] | Jenismessage|reasoning|function_call|other item |
Medanoutput[].content[] | Jeniscontent part |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
claude-sonnet-latestClaude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/chat/completionstext, imagetext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had claude-sonnet-latest | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had Contoh: system, user, assistant, tool, function Syarat: Peranan ini diwakili secara eksplisit oleh jenis keserasian dalam kod sumber; set peranan model yang tepat kekal khusus untuk setiap model. Bergantung pada model | Lalai— |
Medanmessages[].content | Jenisstring|content_part[]|null | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].tool_calls | Jenistool_call[] | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].tool_call_id | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: role=tool; gunakan ID sebenar yang dipulangkan | Lalai— |
Medanmessages[].name | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].reasoning_content | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: Hantar semula penaakulan sebenar yang dipulangkan apabila model ini menggunakannya | Lalai— |
Medanmessages[].reasoning_details | Jenisobject|array | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanmax_completion_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medanstream_options.include_usage | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstop | Jenisstring|string[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenisfunction_tool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].type | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].function.name | Jenisstring | KeperluanDiperlukan | Nilai & had Terpakai apabila: alat fungsi dibekalkan | Lalai— |
Medantools[].function.description | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medantools[].function.parameters | JenisJSON_schema | KeperluanPilihan | Nilai & had | Lalai— |
Medantool_choice | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanparallel_tool_calls | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanresponse_format | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning_effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanservice_tier | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medaninstructions | Jenisstring | KeperluanPilihan | Nilai & had Medan keserasian | Lalai— |
Medanfunctions | Jenisfunction[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanfunction_call | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_options | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Teks · Teks UTF8
messages[].content
Teks + imej
{type:image_url,image_url:{url,detail}}
| Parameter | Kontrak |
|---|---|
Parametermaterial_types | Kontraktext, image |
Parameterrequired | Kontrakmodel, messages |
Parameteroptional_controls | Kontraknull |
Parametercontent_type | Kontrakapplication/json |
Parametertext_file_material | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan |
Kitar hayat: exact retained published method
Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.
POST /v1/chat/completionsAiloot 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 →
Jadual rujukan peringkat model tidak mengubah pemilihan kaedah, sebut harga atau caj. Caj aktif menggunakan kadar kaedah Ailoot yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiOpenAI Chat · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiOpenAI Chat · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Kadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiAnthropic Messages · Tahap 1 berdasarkan panjang permintaan | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
set -eu\n: "${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": "claude-sonnet-latest", "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/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nset -eu\n: "${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": "claude-sonnet-latest", "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_url","image_url":{"url":"data:"+mime+";base64,"+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/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nresp = client.chat.completions.create(\n model="claude-sonnet-latest",\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(resp.to_dict(mode="json"))\nimport OpenAI from "openai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst resp = await client.chat.completions.create({\n model: "claude-sonnet-latest",\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(resp);\nfrom openai import OpenAI\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=OpenAI(base_url="https://api.ailoot.ai/v1",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.chat.completions.stream(model='claude-sonnet-latest',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_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanchoices[].index | Jenisinteger |
Medanchoices[].message.content | Jenisstring|null |
Medanchoices[].message.tool_calls | Jenistool_call[] |
Medanchoices[].message.reasoning_content | Jenisstring |
Medanchoices[].message.reasoning_details | Jenisobject|array |
Medanchoices[].finish_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
anthropic/claude-sonnet-5Claude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/chat/completionstexttext| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had anthropic/claude-sonnet-5 | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had Contoh: system, user, assistant, tool, function Syarat: Peranan ini diwakili secara eksplisit oleh jenis keserasian dalam kod sumber; set peranan model yang tepat kekal khusus untuk setiap model. Bergantung pada model | Lalai— |
Medanmessages[].content | Jenisstring|content_part[]|null | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].tool_calls | Jenistool_call[] | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].tool_call_id | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: role=tool; gunakan ID sebenar yang dipulangkan | Lalai— |
Medanmessages[].name | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medanmessages[].reasoning_content | Jenisstring | KeperluanPilihan | Nilai & had Terpakai apabila: Hantar semula penaakulan sebenar yang dipulangkan apabila model ini menggunakannya | Lalai— |
Medanmessages[].reasoning_details | Jenisobject|array | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanmax_completion_tokens | Jenisinteger | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Medanstream_options.include_usage | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantemperature | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantop_p | Jenisnumber | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanstop | Jenisstring|string[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools | Jenisfunction_tool[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medantools[].type | Jenisstring | KeperluanPilihan | Nilai & had function Bergantung pada model | Lalai— |
Medantools[].function.name | Jenisstring | KeperluanDiperlukan | Nilai & had Terpakai apabila: alat fungsi dibekalkan | Lalai— |
Medantools[].function.description | Jenisstring | KeperluanPilihan | Nilai & had | Lalai— |
Medantools[].function.parameters | JenisJSON_schema | KeperluanPilihan | Nilai & had | Lalai— |
Medantool_choice | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanparallel_tool_calls | Jenisboolean | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanresponse_format | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning_effort | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanreasoning | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanservice_tier | Jenisstring | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medaninstructions | Jenisstring | KeperluanPilihan | Nilai & had Medan keserasian | Lalai— |
Medanfunctions | Jenisfunction[] | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanfunction_call | Jenisstring|object | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanprompt_cache_options | Jenisobject | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— |
Medanmodalities | Jenisstring[] | KeperluanPilihan | Nilai & had text | Lalai— |
Medanprovider | Jenisobject | KeperluanPilihan | Nilai & had Syarat: provider.options ditolak pada laluan teks Ultra ini | Lalai— |
Medancache_control.ttl | Jenisstring | KeperluanPilihan | Nilai & had Syarat: 1h ditolak pada lokasi cache prompt yang digunakan | Lalai— |
Teks · Teks UTF8
messages[].content
| Parameter | Kontrak |
|---|---|
Parametercontent_type | Kontrakapplication/json |
Parameterrequired | Kontrakmodel, messages |
Parametermaterial_types | Kontraktext |
Parametermessages | Kontraktatasusunan mesej dengan struktur role/content |
Parameterfile_material | Kontrakdokumen UTF-8 sebenar dimasukkan sebagai teks literal; ID fail tidak diandaikan |
Parameteroptional_controls | Kontrakstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Kontrakkhusus model; alat fungsi pengguna berbeza daripada alat yang dihoskan |
Parameteroutput_modalities | Kontraktext |
Parameterhosted_operations | Kontrakfalse |
Parametermultimodal_input_binding | Kontraknull |
Kitar hayat: synchronous_or_streamed
Hantar ID model yang tepat bersama messages.
Panjang konteks bukan had output.
Fail teks boleh membekalkan teks literal; input imej/audio/objek fail memerlukan pengikatan tepat.
Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiKadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiTahap panjang permintaan 1 | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "anthropic/claude-sonnet-5", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\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/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='anthropic/claude-sonnet-5',messages=[{"role":"user","content":"Explain observations and hypotheses 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(result.to_dict(mode="json"))\nfrom openai import OpenAI\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=OpenAI(base_url="https://api.ailoot.ai/v1",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.chat.completions.stream(model='anthropic/claude-sonnet-5',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_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanchoices[].index | Jenisinteger |
Medanchoices[].message.content | Jenisstring|null |
Medanchoices[].message.tool_calls | Jenistool_call[] |
Medanchoices[].message.reasoning_content | Jenisstring |
Medanchoices[].message.reasoning_details | Jenisobject|array |
Medanchoices[].finish_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
anthropic/claude-sonnet-5Claude Sonnet 5 untuk analisis teks dan visual berterusan, dengan kaedah perbualan Core menyenaraikan konteks 1,000,000 token serta had output 131,072 token.
Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.
Entri anthropic/claude-sonnet-5, iaitu Claude Sonnet 5, menyokong perbualan segerak atau distrim untuk kerja yang berkembang daripada bukti dibekalkan. Tugasan perisian konkrit ialah menyemak kod sumber bersama tangkap layar, mencadangkan pembetulan tertumpu dan menerangkan cara memeriksa kecacatan asal. Kaedah Core menerima teks serta imej, dengan konteks disenaraikan 1,000,000 token dan had output 131,072 token. Had konteks serta jawapan ialah batasan berasingan.
Pilih kaedah sepadan klien: Messages, Responses atau Chat Completions. Varian Core meminta claude-sonnet-5, claude-sonnet atau claude-sonnet-latest; varian Ultra menggunakan ID diisytiharkan sendiri serta menyenaraikan input teks sahaja. Pilih kaedah Core menerima imej apabila tangkap layar penting, bukan memindahkan sokongan visual kepada kaedah Ultra daripada metadata keluarga. Messages memerlukan max_tokens.
Huraian model diterima mempersembahkan penaakulan adaptif untuk analisis lebih mendalam. Medan API bergantung pada antara muka: output_config.effort, reasoning.effort dan reasoning_effort ialah medan kaedah berbeza dengan sokongan bergantung pada model. Alat aplikasi melaksanakan tindakan cadangan serta memulangkan pemerhatian sebenar; teks model sahaja tidak menerapkan patch atau menjalankan pelayar. Simpan semua blok respons dan delta distrim ketika mengumpulkan hasil. Entri biasa ini mengikuti kitar hayat perbualan; tugasan pukal bebas tertunda sesuai pada entri :batch ditakrif berasingan.
POST /v1/messagestextjson, sse| Medan | Jenis | Keperluan | Nilai & had | Lalai |
|---|---|---|---|---|
Medanmodel | Jenisstring | KeperluanDiperlukan | Nilai & had anthropic/claude-sonnet-5 | Lalai— |
Medanmessages | Jenismessage[] | KeperluanDiperlukan | Nilai & had | Lalai— |
Medanmessages[].role | Jenisstring | KeperluanDiperlukan | Nilai & had user, assistant | Lalai— |
Medanmessages[].content | Jenisstring | KeperluanDiperlukan | Nilai & had | Lalai— |
Medanmax_tokens | Jenisinteger | KeperluanDiperlukan | Nilai & had | Lalai— |
Medanstream | Jenisboolean | KeperluanPilihan | Nilai & had | Lalaifalse |
Teks · Teks UTF-8
rentetan messages[].content
| Parameter | Kontrak |
|---|---|
Parametercontent_type | Kontrakapplication/json |
Parameterrequired | Kontrakmodel, messages, max_tokens |
Parameteroptional_controls | Kontrakstream |
Parametermaterial_types | Kontraktext |
Parametertext_file_material | KontrakBaca dokumen UTF-8 sebenar dan letakkan teksnya dalam rentetan input atau kandungan mesej yang ditunjukkan. |
Kitar hayat: synchronous_or_streamed
Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.
Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.
| Konfigurasi | Jadual kadar | Token konteks, melebihi | Token konteks, sehingga dan termasuk | Caj |
|---|---|---|---|---|
| KonfigurasiKadar asas token | Jadual kadarKadar asas | Token konteks, melebihi— | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
| KonfigurasiTahap panjang permintaan 1 | Jadual kadar— | Token konteks, melebihi0 | Token konteks, sehingga dan termasukTiada had atas | Caj Input · Token input $0.004 / 1K token Output · Token output $0.02 / 1K token Input · Bacaan cache $0.0004 / 1K token Input · Penulisan cache $0.005 / 1K token Input · Penulisan cache, 1 jam $0.008 / 1K token |
Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →
{\n "model": "anthropic/claude-sonnet-5",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'anthropic/claude-sonnet-5', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'anthropic/claude-sonnet-5', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON atau SSE
Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.
Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output.
Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan.
| Medan | Jenis |
|---|---|
Medanid | Jenisstring |
Medanmodel | Jenisstring |
Medancontent[] | Jenistext|thinking|tool_use|other content block |
Medanstop_reason | Jenisstring|null |
Medanusage | Jenisobject |
| Respons | Syarat | Langkah seterusnya |
|---|---|---|
Respons400 · invalid_request_error | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. |
Ailoot claude-sonnet-5, claude-sonnet, claude-sonnet-latest
Ailoot Ultra claude-sonnet-5, anthropic/claude-sonnet-5
/chat/completions
anthropic/claude-sonnet-5, claude-sonnet, claude-sonnet-latest, claude-sonnet-5, claude-sonnet-5
Contoh teknikal dalam bahasa Inggeris
Diagnose duplicated webhook effects\n\nReview this flow: receive event; charge wallet; save event_id. If saving fails after charging, redelivery charges again. Propose a corrected transaction boundary using a unique event_id record and the wallet update. State the expected behavior for first delivery, duplicate delivery and failure before commit. Return pseudocode and checks; do not claim to execute them.Transaction pseudocode and a three-case behavior table for first, duplicate and rolled-back delivery.The deduplication record and wallet effect commit together, and a duplicate produces no second debit.