Doubao 1.5 Pro combines general language understanding with knowledge, programming, reasoning and Chinese-language capabilities. This card represents the Pro 256K variant in the Doubao 1.5 family. Its practical role is to turn a defined question and the relevant text into a coherent result, such as a comparison, an implementation proposal or a document prepared for a particular audience.
The Pro design. The producer’s technical description centres on a sparse mixture-of-experts network and the joint development of training and inference. Post-training combines supervised learning, reward modelling and reinforcement learning across knowledge, mathematics, code and dialogue. These are family development characteristics; the task should still specify exactly which supplied facts and requirements the answer must preserve.
For analysis across several sections, introduce the material with a small map: the purpose of each section, its version and the question it should help answer. Put clear labels around individual documents, and retain original headings and identifiers. Ask for a comparison table that gives the requirement, the supporting section and any disagreement with another source. A useful final artifact might identify changed obligations between two revisions or gather all prerequisites for one proposed workflow.
For a technical planning task, provide the desired user behaviour, existing constraints and the relevant implementation notes. Request a proposed sequence of changes, a mapping from each change to its requirement and the unresolved questions that affect the decision. For coding, add the related functions and interfaces rather than only the line that fails. Ask the model to explain interactions between the parts and propose a bounded edit, then evaluate that proposal with the project’s real checks.
For Chinese-language documents, include the terminology that must remain consistent, the intended audience and the distinction between quotations and your own commentary. Ask for a report with a concise opening, an evidence section and action items tied to the source labels. A later message can narrow the report to one department, change the level of detail or request an alternative wording while keeping the same factual material.
Prepare large collections around relevance, not volume. Remove duplicate boilerplate, keep sections that qualify an important claim and state which source wins when versions conflict. The requested answer also needs space, so define its scope and structure before supplying a large body of material. When an answer depends on a table, keep its column labels and units with the values. Review the final synthesis for omitted exceptions and ask the model to identify the exact section behind a consequential conclusion.
", "aboutText": "Doubao 1.5 Pro combines general language understanding with knowledge, programming, reasoning and Chinese-language capabilities. This card represents the Pro 256K variant in the Doubao 1.5 family. Its practical role is to turn a defined question and the relevant text into a coherent result, such as a comparison, an implementation proposal or a document prepared for a particular audience.\n\nThe Pro design. The producer’s technical description centres on a sparse mixture-of-experts network and the joint development of training and inference. Post-training combines supervised learning, reward modelling and reinforcement learning across knowledge, mathematics, code and dialogue. These are family development characteristics; the task should still specify exactly which supplied facts and requirements the answer must preserve.\n\nFor analysis across several sections, introduce the material with a small map: the purpose of each section, its version and the question it should help answer. Put clear labels around individual documents, and retain original headings and identifiers. Ask for a comparison table that gives the requirement, the supporting section and any disagreement with another source. A useful final artifact might identify changed obligations between two revisions or gather all prerequisites for one proposed workflow.\n\nFor a technical planning task, provide the desired user behaviour, existing constraints and the relevant implementation notes. Request a proposed sequence of changes, a mapping from each change to its requirement and the unresolved questions that affect the decision. For coding, add the related functions and interfaces rather than only the line that fails. Ask the model to explain interactions between the parts and propose a bounded edit, then evaluate that proposal with the project’s real checks.\n\nFor Chinese-language documents, include the terminology that must remain consistent, the intended audience and the distinction between quotations and your own commentary. Ask for a report with a concise opening, an evidence section and action items tied to the source labels. A later message can narrow the report to one department, change the level of detail or request an alternative wording while keeping the same factual material.\n\nPrepare large collections around relevance, not volume. Remove duplicate boilerplate, keep sections that qualify an important claim and state which source wins when versions conflict. The requested answer also needs space, so define its scope and structure before supplying a large body of material. When an answer depends on a table, keep its column labels and units with the values. Review the final synthesis for omitted exceptions and ask the model to identify the exact section behind a consequential conclusion.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Summarize Chinese handover rules\n\nPrepare an English handover note from these Chinese policy sections. A: 仓库每天16:00停止接收当日发货单。 B: 易碎品必须加贴“易碎”标签。 C: 加急订单也须遵守16:00截止时间。 Include the cutoff, packaging requirement and urgent-order exception status. Cite section letters and preserve the Chinese label exactly.A three-row handover table with rule, supporting section and operational instruction in English.Check that urgent orders retain the 16:00 cutoff and the required label remains 易碎.Doubao-1.5-pro-256kDoubao 1.5 Pro 256K is ByteDance’s text model for knowledge, reasoning, code and Chinese-language tasks. Prepare related source sections together and ask for a comparison, a requirements document or an explanation with references to your material.
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 combines general language understanding with knowledge, programming, reasoning and Chinese-language capabilities. This card represents the Pro 256K variant in the Doubao 1.5 family. Its practical role is to turn a defined question and the relevant text into a coherent result, such as a comparison, an implementation proposal or a document prepared for a particular audience.
The Pro design. The producer’s technical description centres on a sparse mixture-of-experts network and the joint development of training and inference. Post-training combines supervised learning, reward modelling and reinforcement learning across knowledge, mathematics, code and dialogue. These are family development characteristics; the task should still specify exactly which supplied facts and requirements the answer must preserve.
For analysis across several sections, introduce the material with a small map: the purpose of each section, its version and the question it should help answer. Put clear labels around individual documents, and retain original headings and identifiers. Ask for a comparison table that gives the requirement, the supporting section and any disagreement with another source. A useful final artifact might identify changed obligations between two revisions or gather all prerequisites for one proposed workflow.
For a technical planning task, provide the desired user behaviour, existing constraints and the relevant implementation notes. Request a proposed sequence of changes, a mapping from each change to its requirement and the unresolved questions that affect the decision. For coding, add the related functions and interfaces rather than only the line that fails. Ask the model to explain interactions between the parts and propose a bounded edit, then evaluate that proposal with the project’s real checks.
For Chinese-language documents, include the terminology that must remain consistent, the intended audience and the distinction between quotations and your own commentary. Ask for a report with a concise opening, an evidence section and action items tied to the source labels. A later message can narrow the report to one department, change the level of detail or request an alternative wording while keeping the same factual material.
Prepare large collections around relevance, not volume. Remove duplicate boilerplate, keep sections that qualify an important claim and state which source wins when versions conflict. The requested answer also needs space, so define its scope and structure before supplying a large body of material. When an answer depends on a table, keep its column labels and units with the values. Review the final synthesis for omitted exceptions and ask the model to identify the exact section behind a consequential conclusion.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits Doubao-1.5-pro-256k | 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 $0.00176 / 1K tokens Output · Output tokens $0.00286 / 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-256k", "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-256k',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-256k',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-256k
Use the method paths shown above.
Doubao-1.5-pro-256kDoubao 1.5 Pro 256K is ByteDance’s text model for knowledge, reasoning, code and Chinese-language tasks. Prepare related source sections together and ask for a comparison, a requirements document or an explanation with references to your material.
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 combines general language understanding with knowledge, programming, reasoning and Chinese-language capabilities. This card represents the Pro 256K variant in the Doubao 1.5 family. Its practical role is to turn a defined question and the relevant text into a coherent result, such as a comparison, an implementation proposal or a document prepared for a particular audience.
The Pro design. The producer’s technical description centres on a sparse mixture-of-experts network and the joint development of training and inference. Post-training combines supervised learning, reward modelling and reinforcement learning across knowledge, mathematics, code and dialogue. These are family development characteristics; the task should still specify exactly which supplied facts and requirements the answer must preserve.
For analysis across several sections, introduce the material with a small map: the purpose of each section, its version and the question it should help answer. Put clear labels around individual documents, and retain original headings and identifiers. Ask for a comparison table that gives the requirement, the supporting section and any disagreement with another source. A useful final artifact might identify changed obligations between two revisions or gather all prerequisites for one proposed workflow.
For a technical planning task, provide the desired user behaviour, existing constraints and the relevant implementation notes. Request a proposed sequence of changes, a mapping from each change to its requirement and the unresolved questions that affect the decision. For coding, add the related functions and interfaces rather than only the line that fails. Ask the model to explain interactions between the parts and propose a bounded edit, then evaluate that proposal with the project’s real checks.
For Chinese-language documents, include the terminology that must remain consistent, the intended audience and the distinction between quotations and your own commentary. Ask for a report with a concise opening, an evidence section and action items tied to the source labels. A later message can narrow the report to one department, change the level of detail or request an alternative wording while keeping the same factual material.
Prepare large collections around relevance, not volume. Remove duplicate boilerplate, keep sections that qualify an important claim and state which source wins when versions conflict. The requested answer also needs space, so define its scope and structure before supplying a large body of material. When an answer depends on a table, keep its column labels and units with the values. Review the final synthesis for omitted exceptions and ask the model to identify the exact section behind a consequential conclusion.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits Doubao-1.5-pro-256k | 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 $0.00176 / 1K tokens Output · Output tokens $0.00286 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "Doubao-1.5-pro-256k",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-256k', '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-256k', '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-256k
Use the method paths shown above.
Technical example in English
Summarize Chinese handover rules\n\nPrepare an English handover note from these Chinese policy sections. A: 仓库每天16:00停止接收当日发货单。 B: 易碎品必须加贴“易碎”标签。 C: 加急订单也须遵守16:00截止时间。 Include the cutoff, packaging requirement and urgent-order exception status. Cite section letters and preserve the Chinese label exactly.A three-row handover table with rule, supporting section and operational instruction in English.Check that urgent orders retain the 16:00 cutoff and the required label remains 易碎.