v0 1.5 Large is the larger profile in Vercel’s v0 composite model family. It combines a base language model with specialized retrieval and code-editing components. The maker describes this profile as useful for specialized development work and multi-step tasks, giving a physics engine in three.js and database migrations as examples.
Describe the behavior, the existing project and the result to produce. For a physics-engine task, give the movement rules, interactions and expected runtime behavior. For a migration, supply the current and target structures, the data to preserve and the checks that establish a valid result.
Break the requested outcome into related conditions while keeping the final artifact explicit. A model can produce the implementation or proposed migration; the surrounding application executes it and returns the observed behavior. Record those observations with the result when comparing a revision.
The maker’s v0 architecture prepares the conversation and retrieves specialized material such as documentation, UI examples and relevant project sources. The base model handles new generations and substantial changes. Smaller edits can use a dedicated Quick Edit path, while AutoFix examines and corrects generated output during streaming and in a final pass.
Provide relevant existing files and explain how they connect to the change. For a multi-step task, keep the project decisions and completion conditions visible in follow-ups so a local change remains aligned with the intended result.
Inspect the generated artifact in the actual development environment. A larger code change should be checked against the behavior and data conditions named in the brief. A follow-up can supply an error or a specific mismatch and ask for a correction to the affected part.
AutoFix and linting are parts of code preparation. They do not by themselves establish that the application ran, that a migration preserved its data or that a site was deployed. Those outcomes are established through the surrounding application’s execution and observations.
The published profile lists a 512,000-token working context. The maker also distinguishes its larger context from the Medium profile’s window. Organize source material around the task and leave room for the requested answer.
The v0 profile names a composite workflow whose base component can evolve. Keep the exact 1.5 Large identity with each result, and compare completed artifacts rather than assuming that the larger profile guarantees an error-free answer for every task.
", "aboutText": "v0 1.5 Large is the larger profile in Vercel’s v0 composite model family. It combines a base language model with specialized retrieval and code-editing components. The maker describes this profile as useful for specialized development work and multi-step tasks, giving a physics engine in three.js and database migrations as examples.\n\n### Define the larger task and its conditions\n\nDescribe the behavior, the existing project and the result to produce. For a physics-engine task, give the movement rules, interactions and expected runtime behavior. For a migration, supply the current and target structures, the data to preserve and the checks that establish a valid result.\n\nBreak the requested outcome into related conditions while keeping the final artifact explicit. A model can produce the implementation or proposed migration; the surrounding application executes it and returns the observed behavior. Record those observations with the result when comparing a revision.\n\n### Use the composite model’s context\n\nThe maker’s v0 architecture prepares the conversation and retrieves specialized material such as documentation, UI examples and relevant project sources. The base model handles new generations and substantial changes. Smaller edits can use a dedicated Quick Edit path, while AutoFix examines and corrects generated output during streaming and in a final pass.\n\nProvide relevant existing files and explain how they connect to the change. For a multi-step task, keep the project decisions and completion conditions visible in follow-ups so a local change remains aligned with the intended result.\n\n### Review code and application behavior\n\nInspect the generated artifact in the actual development environment. A larger code change should be checked against the behavior and data conditions named in the brief. A follow-up can supply an error or a specific mismatch and ask for a correction to the affected part.\n\nAutoFix and linting are parts of code preparation. They do not by themselves establish that the application ran, that a migration preserved its data or that a site was deployed. Those outcomes are established through the surrounding application’s execution and observations.\n\n### Keep the Large profile distinct\n\nThe published profile lists a 512,000-token working context. The maker also distinguishes its larger context from the Medium profile’s window. Organize source material around the task and leave room for the requested answer.\n\nThe v0 profile names a composite workflow whose base component can evolve. Keep the exact 1.5 Large identity with each result, and compare completed artifacts rather than assuming that the larger profile guarantees an error-free answer for every task.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Propose a value-preserving migration\n\nPlan a PostgreSQL migration from orders(id, total_cents) to orders(id, amount_cents), preserving every row, identifier and nullable monetary value. Provide proposed migration SQL, the consumer update sequence and before/after checks for row count and values. No execution environment is supplied; distinguish the proposal from a completed migration and do not convert cents to another unit.A proposed migration artifact, coordinated consumer steps and explicit data-preservation checks.No row, null value or cent amount may be silently dropped, filled or rescaled.v0-1.5-lgv0 1.5 Large is Vercel’s larger composite profile for web code and multi-step development tasks. Its maker highlights specialized work such as a 3D physics engine or database migration, with retrieval, Quick Edit and AutoFix around the base model.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
v0 1.5 Large is the larger profile in Vercel’s v0 composite model family. It combines a base language model with specialized retrieval and code-editing components. The maker describes this profile as useful for specialized development work and multi-step tasks, giving a physics engine in three.js and database migrations as examples.
Describe the behavior, the existing project and the result to produce. For a physics-engine task, give the movement rules, interactions and expected runtime behavior. For a migration, supply the current and target structures, the data to preserve and the checks that establish a valid result.
Break the requested outcome into related conditions while keeping the final artifact explicit. A model can produce the implementation or proposed migration; the surrounding application executes it and returns the observed behavior. Record those observations with the result when comparing a revision.
The maker’s v0 architecture prepares the conversation and retrieves specialized material such as documentation, UI examples and relevant project sources. The base model handles new generations and substantial changes. Smaller edits can use a dedicated Quick Edit path, while AutoFix examines and corrects generated output during streaming and in a final pass.
Provide relevant existing files and explain how they connect to the change. For a multi-step task, keep the project decisions and completion conditions visible in follow-ups so a local change remains aligned with the intended result.
Inspect the generated artifact in the actual development environment. A larger code change should be checked against the behavior and data conditions named in the brief. A follow-up can supply an error or a specific mismatch and ask for a correction to the affected part.
AutoFix and linting are parts of code preparation. They do not by themselves establish that the application ran, that a migration preserved its data or that a site was deployed. Those outcomes are established through the surrounding application’s execution and observations.
The published profile lists a 512,000-token working context. The maker also distinguishes its larger context from the Medium profile’s window. Organize source material around the task and leave room for the requested answer.
The v0 profile names a composite workflow whose base component can evolve. Keep the exact 1.5 Large identity with each result, and compare completed artifacts rather than assuming that the larger profile guarantees an error-free answer for every task.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits v0-1.5-lg | 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 $0.033 / 1K tokens Output · Output tokens $0.165 / 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": "v0-1.5-lg", "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='v0-1.5-lg',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='v0-1.5-lg',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 v0-1.5-lg
Use the method paths shown above.
v0-1.5-lgv0 1.5 Large is Vercel’s larger composite profile for web code and multi-step development tasks. Its maker highlights specialized work such as a 3D physics engine or database migration, with retrieval, Quick Edit and AutoFix around the base model.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
v0 1.5 Large is the larger profile in Vercel’s v0 composite model family. It combines a base language model with specialized retrieval and code-editing components. The maker describes this profile as useful for specialized development work and multi-step tasks, giving a physics engine in three.js and database migrations as examples.
Describe the behavior, the existing project and the result to produce. For a physics-engine task, give the movement rules, interactions and expected runtime behavior. For a migration, supply the current and target structures, the data to preserve and the checks that establish a valid result.
Break the requested outcome into related conditions while keeping the final artifact explicit. A model can produce the implementation or proposed migration; the surrounding application executes it and returns the observed behavior. Record those observations with the result when comparing a revision.
The maker’s v0 architecture prepares the conversation and retrieves specialized material such as documentation, UI examples and relevant project sources. The base model handles new generations and substantial changes. Smaller edits can use a dedicated Quick Edit path, while AutoFix examines and corrects generated output during streaming and in a final pass.
Provide relevant existing files and explain how they connect to the change. For a multi-step task, keep the project decisions and completion conditions visible in follow-ups so a local change remains aligned with the intended result.
Inspect the generated artifact in the actual development environment. A larger code change should be checked against the behavior and data conditions named in the brief. A follow-up can supply an error or a specific mismatch and ask for a correction to the affected part.
AutoFix and linting are parts of code preparation. They do not by themselves establish that the application ran, that a migration preserved its data or that a site was deployed. Those outcomes are established through the surrounding application’s execution and observations.
The published profile lists a 512,000-token working context. The maker also distinguishes its larger context from the Medium profile’s window. Organize source material around the task and leave room for the requested answer.
The v0 profile names a composite workflow whose base component can evolve. Keep the exact 1.5 Large identity with each result, and compare completed artifacts rather than assuming that the larger profile guarantees an error-free answer for every task.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits v0-1.5-lg | 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 $0.033 / 1K tokens Output · Output tokens $0.165 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "v0-1.5-lg",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'v0-1.5-lg', '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': 'v0-1.5-lg', '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 v0-1.5-lg
Use the method paths shown above.
Technical example in English
Propose a value-preserving migration\n\nPlan a PostgreSQL migration from orders(id, total_cents) to orders(id, amount_cents), preserving every row, identifier and nullable monetary value. Provide proposed migration SQL, the consumer update sequence and before/after checks for row count and values. No execution environment is supplied; distinguish the proposal from a completed migration and do not convert cents to another unit.A proposed migration artifact, coordinated consumer steps and explicit data-preservation checks.No row, null value or cent amount may be silently dropped, filled or rescaled.