Doubao 1.5 Pro is ByteDance’s general-purpose language model. Its release highlights knowledge-intensive questions, programming, reasoning and Chinese-language understanding. A text request can produce an explanation, a rewritten passage, a comparison of supplied materials or a proposed piece of code. The Pro family brings these tasks together in one conversational model.
Model design. Doubao 1.5 Pro uses a sparse mixture-of-experts architecture: selected parts of the network contribute to processing a token rather than the entire network being activated in the same way. The team developed training and inference together and describes adjustments to model depth, width and expert selection as part of its development approach. For the person preparing a request, the useful choice is a clear task with the information required to complete it, rather than a vague instruction to improve everything.
Training and dialogue. The producer describes supervised fine-tuning, reward modelling and reinforcement learning, with an independent data production process combining human annotation and the model’s own improvement techniques. Its reward framework covers mathematics, programming, knowledge and conversation. These are development methods behind the model’s general-purpose behaviour. They do not replace the source material that a workplace assistant needs for questions about a particular organisation or document.
For a knowledge assistant, paste the relevant policy or technical specification, name the question and state which sections take priority. Ask for an answer with a short justification and section labels next to the supporting facts. A useful request might compare two procedures, identify an exception and prepare a draft reply for the team that owns the process. Keep background facts and the requested decision in separate parts of the prompt so the model can use both without treating a draft suggestion as an approved rule.
For coding, supply the function or module, its expected behaviour, the observed problem and representative input. Request the smallest proposed change, an explanation of why it addresses the problem and examples that exercise the changed branch. For Chinese-language work, include specialised terminology, the intended audience and a sample of the desired style. Ask for a revised passage plus a brief list of substantive changes; this makes terminology and meaning easier to review than an unexplained rewrite.
For a document editor, specify what must survive the revision: names, numbers, definitions, headings and mandatory conclusions. Set the final artifact explicitly, such as a customer reply, a technical note or a table of requirements. When the supplied material omits a fact, ask the model to mark the missing item and continue with the supported portions. Review generated code in its actual project and check decisive factual statements against the materials you supplied.
", "aboutText": "Doubao 1.5 Pro is ByteDance’s general-purpose language model. Its release highlights knowledge-intensive questions, programming, reasoning and Chinese-language understanding. A text request can produce an explanation, a rewritten passage, a comparison of supplied materials or a proposed piece of code. The Pro family brings these tasks together in one conversational model.\n\nModel design. Doubao 1.5 Pro uses a sparse mixture-of-experts architecture: selected parts of the network contribute to processing a token rather than the entire network being activated in the same way. The team developed training and inference together and describes adjustments to model depth, width and expert selection as part of its development approach. For the person preparing a request, the useful choice is a clear task with the information required to complete it, rather than a vague instruction to improve everything.\n\nTraining and dialogue. The producer describes supervised fine-tuning, reward modelling and reinforcement learning, with an independent data production process combining human annotation and the model’s own improvement techniques. Its reward framework covers mathematics, programming, knowledge and conversation. These are development methods behind the model’s general-purpose behaviour. They do not replace the source material that a workplace assistant needs for questions about a particular organisation or document.\n\nFor a knowledge assistant, paste the relevant policy or technical specification, name the question and state which sections take priority. Ask for an answer with a short justification and section labels next to the supporting facts. A useful request might compare two procedures, identify an exception and prepare a draft reply for the team that owns the process. Keep background facts and the requested decision in separate parts of the prompt so the model can use both without treating a draft suggestion as an approved rule.\n\nFor coding, supply the function or module, its expected behaviour, the observed problem and representative input. Request the smallest proposed change, an explanation of why it addresses the problem and examples that exercise the changed branch. For Chinese-language work, include specialised terminology, the intended audience and a sample of the desired style. Ask for a revised passage plus a brief list of substantive changes; this makes terminology and meaning easier to review than an unexplained rewrite.\n\nFor a document editor, specify what must survive the revision: names, numbers, definitions, headings and mandatory conclusions. Set the final artifact explicitly, such as a customer reply, a technical note or a table of requirements. When the supplied material omits a fact, ask the model to mark the missing item and continue with the supported portions. Review generated code in its actual project and check decisive factual statements against the materials you supplied.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Apply a policy exception\n\nDraft a team reply from this policy. [General] Requests need three working days of notice. [Exception] A broken production printer may be reported immediately. Case: "The production printer stopped this morning; may I open a request today?" Give the answer and cite the applicable section, without inventing repair timing or approval authority.A short reply citing Exception and distinguishing immediate reporting from any repair commitment.Ensure the reply permits reporting today but does not promise same-day repair.Doubao-1.5-pro-32kDoubao 1.5 Pro 32K is ByteDance’s general-purpose text model for knowledge questions, coding, reasoning and Chinese-language work. Use it to explain source material, rewrite a document or propose code from explicit requirements.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Doubao 1.5 Pro is ByteDance’s general-purpose language model. Its release highlights knowledge-intensive questions, programming, reasoning and Chinese-language understanding. A text request can produce an explanation, a rewritten passage, a comparison of supplied materials or a proposed piece of code. The Pro family brings these tasks together in one conversational model.
Model design. Doubao 1.5 Pro uses a sparse mixture-of-experts architecture: selected parts of the network contribute to processing a token rather than the entire network being activated in the same way. The team developed training and inference together and describes adjustments to model depth, width and expert selection as part of its development approach. For the person preparing a request, the useful choice is a clear task with the information required to complete it, rather than a vague instruction to improve everything.
Training and dialogue. The producer describes supervised fine-tuning, reward modelling and reinforcement learning, with an independent data production process combining human annotation and the model’s own improvement techniques. Its reward framework covers mathematics, programming, knowledge and conversation. These are development methods behind the model’s general-purpose behaviour. They do not replace the source material that a workplace assistant needs for questions about a particular organisation or document.
For a knowledge assistant, paste the relevant policy or technical specification, name the question and state which sections take priority. Ask for an answer with a short justification and section labels next to the supporting facts. A useful request might compare two procedures, identify an exception and prepare a draft reply for the team that owns the process. Keep background facts and the requested decision in separate parts of the prompt so the model can use both without treating a draft suggestion as an approved rule.
For coding, supply the function or module, its expected behaviour, the observed problem and representative input. Request the smallest proposed change, an explanation of why it addresses the problem and examples that exercise the changed branch. For Chinese-language work, include specialised terminology, the intended audience and a sample of the desired style. Ask for a revised passage plus a brief list of substantive changes; this makes terminology and meaning easier to review than an unexplained rewrite.
For a document editor, specify what must survive the revision: names, numbers, definitions, headings and mandatory conclusions. Set the final artifact explicitly, such as a customer reply, a technical note or a table of requirements. When the supplied material omits a fact, ask the model to mark the missing item and continue with the supported portions. Review generated code in its actual project and check decisive factual statements against the materials you supplied.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits Doubao-1.5-pro-32k | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.000264 / 1K tokens Output · Output tokens $0.000638 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "Doubao-1.5-pro-32k", "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='Doubao-1.5-pro-32k',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='Doubao-1.5-pro-32k',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 or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra Doubao-1.5-pro-32k
Use the method paths shown above.
Doubao-1.5-pro-32kDoubao 1.5 Pro 32K is ByteDance’s general-purpose text model for knowledge questions, coding, reasoning and Chinese-language work. Use it to explain source material, rewrite a document or propose code from explicit requirements.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Doubao 1.5 Pro is ByteDance’s general-purpose language model. Its release highlights knowledge-intensive questions, programming, reasoning and Chinese-language understanding. A text request can produce an explanation, a rewritten passage, a comparison of supplied materials or a proposed piece of code. The Pro family brings these tasks together in one conversational model.
Model design. Doubao 1.5 Pro uses a sparse mixture-of-experts architecture: selected parts of the network contribute to processing a token rather than the entire network being activated in the same way. The team developed training and inference together and describes adjustments to model depth, width and expert selection as part of its development approach. For the person preparing a request, the useful choice is a clear task with the information required to complete it, rather than a vague instruction to improve everything.
Training and dialogue. The producer describes supervised fine-tuning, reward modelling and reinforcement learning, with an independent data production process combining human annotation and the model’s own improvement techniques. Its reward framework covers mathematics, programming, knowledge and conversation. These are development methods behind the model’s general-purpose behaviour. They do not replace the source material that a workplace assistant needs for questions about a particular organisation or document.
For a knowledge assistant, paste the relevant policy or technical specification, name the question and state which sections take priority. Ask for an answer with a short justification and section labels next to the supporting facts. A useful request might compare two procedures, identify an exception and prepare a draft reply for the team that owns the process. Keep background facts and the requested decision in separate parts of the prompt so the model can use both without treating a draft suggestion as an approved rule.
For coding, supply the function or module, its expected behaviour, the observed problem and representative input. Request the smallest proposed change, an explanation of why it addresses the problem and examples that exercise the changed branch. For Chinese-language work, include specialised terminology, the intended audience and a sample of the desired style. Ask for a revised passage plus a brief list of substantive changes; this makes terminology and meaning easier to review than an unexplained rewrite.
For a document editor, specify what must survive the revision: names, numbers, definitions, headings and mandatory conclusions. Set the final artifact explicitly, such as a customer reply, a technical note or a table of requirements. When the supplied material omits a fact, ask the model to mark the missing item and continue with the supported portions. Review generated code in its actual project and check decisive factual statements against the materials you supplied.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits Doubao-1.5-pro-32k | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.000264 / 1K tokens Output · Output tokens $0.000638 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "Doubao-1.5-pro-32k",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'Doubao-1.5-pro-32k', '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': 'Doubao-1.5-pro-32k', '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 or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra Doubao-1.5-pro-32k
Use the method paths shown above.
Technical example in English
Apply a policy exception\n\nDraft a team reply from this policy. [General] Requests need three working days of notice. [Exception] A broken production printer may be reported immediately. Case: "The production printer stopped this morning; may I open a request today?" Give the answer and cite the applicable section, without inventing repair timing or approval authority.A short reply citing Exception and distinguishing immediate reporting from any repair commitment.Ensure the reply permits reporting today but does not promise same-day repair.