Trail Pack
- Weight
- 1.2 kg
- Rain cover
- Side pocket
Schematron V2 Turbo is Inference.net’s throughput-oriented model for schema-guided extraction from HTML. It is purpose-built for converting messy web pages into clean, typed JSON, rather than a general conversational assistant. The task has two semantic inputs: the HTML to inspect and a schema defining the information to extract. The result is a JSON object conforming to that structure.
The manufacturer’s workflow uses the schema instead of a natural-language user or system instruction as the extraction control. Put the field meaning in its description, with the appropriate type and any nested object or list structure. For a product page, a practical schema might request the product name, specification values with units and a list of supplied features. This example describes the desired record; the values should come from the page itself.
A clear field description distinguishes similar source values. Identify whether a field means the current product name, a technical identifier or a category, and explain how missing information should be represented. The maker demonstrates typed schema definitions and JSON-schema output, with validation of the returned object. Correct syntax does not itself establish that an extracted value agrees with the original page.
Inference.net recommends cleaning HTML to remove scripts, styles and boilerplate before extraction, using preprocessing that matches the training data. Trim to the relevant region where possible, while erring on the side of keeping more content when aggressive removal could discard useful evidence. Preserve headings, table relationships, labels and units needed to interpret a field.
For a page containing several products, make the desired item clear in the material and schema. Keep a source URL or page identifier in the surrounding task record so the extracted object can be checked later. Fetching the page is a separate operation performed by the application; Schematron extracts from the HTML it receives.
V2 Turbo is the version optimised for processing many pages with a defined output structure. V2 Small is the distinct extraction-quality option for complex schemas and long documents. Choose the workload role according to the actual records required, and evaluate how well the returned data preserves the information in representative pages. A high-throughput role does not make an unclear field definition precise.
The producer’s V2 family announcement documents a 128K context and strict JSON-mode schema adherence. HTML and the requested result share the available task budget. Large pages benefit from identifying the relevant content instead of using unrelated page structure as context.
The JSON output can feed a catalogue, ingestion task or a search system that needs specific facts instead of an entire raw page. Define the record once, compare extracted values with source examples and retain the source association. For a numeric field, check its units and whether the page describes a value, range or alternative configuration.
The V2 model remains separate from the original open-weight Schematron releases. Its documented extraction mode does not imply general chat, image analysis, autonomous browsing or tool execution. The useful deliverable is the requested typed record grounded in the supplied HTML, with unresolved or absent source information represented according to the schema.
", "aboutText": "Schematron V2 Turbo is Inference.net’s throughput-oriented model for schema-guided extraction from HTML. It is purpose-built for converting messy web pages into clean, typed JSON, rather than a general conversational assistant. The task has two semantic inputs: the HTML to inspect and a schema defining the information to extract. The result is a JSON object conforming to that structure.\n\n### What defines the extraction\n\nThe manufacturer’s workflow uses the schema instead of a natural-language user or system instruction as the extraction control. Put the field meaning in its description, with the appropriate type and any nested object or list structure. For a product page, a practical schema might request the product name, specification values with units and a list of supplied features. This example describes the desired record; the values should come from the page itself.\n\nA clear field description distinguishes similar source values. Identify whether a field means the current product name, a technical identifier or a category, and explain how missing information should be represented. The maker demonstrates typed schema definitions and JSON-schema output, with validation of the returned object. Correct syntax does not itself establish that an extracted value agrees with the original page.\n\n### Prepare the HTML\n\nInference.net recommends cleaning HTML to remove scripts, styles and boilerplate before extraction, using preprocessing that matches the training data. Trim to the relevant region where possible, while erring on the side of keeping more content when aggressive removal could discard useful evidence. Preserve headings, table relationships, labels and units needed to interpret a field.\n\nFor a page containing several products, make the desired item clear in the material and schema. Keep a source URL or page identifier in the surrounding task record so the extracted object can be checked later. Fetching the page is a separate operation performed by the application; Schematron extracts from the HTML it receives.\n\n### Turbo’s workload role\n\nV2 Turbo is the version optimised for processing many pages with a defined output structure. V2 Small is the distinct extraction-quality option for complex schemas and long documents. Choose the workload role according to the actual records required, and evaluate how well the returned data preserves the information in representative pages. A high-throughput role does not make an unclear field definition precise.\n\nThe producer’s V2 family announcement documents a 128K context and strict JSON-mode schema adherence. HTML and the requested result share the available task budget. Large pages benefit from identifying the relevant content instead of using unrelated page structure as context.\n\n### A useful extracted result\n\nThe JSON output can feed a catalogue, ingestion task or a search system that needs specific facts instead of an entire raw page. Define the record once, compare extracted values with source examples and retain the source association. For a numeric field, check its units and whether the page describes a value, range or alternative configuration.\n\nThe V2 model remains separate from the original open-weight Schematron releases. Its documented extraction mode does not imply general chat, image analysis, autonomous browsing or tool execution. The useful deliverable is the requested typed record grounded in the supplied HTML, with unresolved or absent source information represented according to the schema.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Extract a typed product record\n\nHTML:\n<article><h1>Trail Pack</h1><dl><dt>Weight</dt><dd>1.2 kg</dd></dl><ul><li>Rain cover</li><li>Side pocket</li></ul></article>\nSchema:\n{"type":"object","properties":{"name":{"type":"string","description":"Product heading"},"weight_kg":{"type":"number","description":"Weight in kilograms"},"features":{"type":"array","items":{"type":"string"},"description":"Listed product features"}},"required":["name","weight_kg","features"],"additionalProperties":false}A JSON object with name as a string, weight_kg as a number and features as a list of strings, with no extra properties.Validate the requested schema and compare every value to the supplied HTML, including the kilogram unit.inference-net/schematron-v2-turboSchematron V2 Turbo turns supplied HTML into typed JSON using a caller-defined schema. The throughput-oriented V2 edition supports long-page extraction in a 128K producer context; field descriptions, nested objects and lists define the information to return.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Schematron V2 Turbo is Inference.net’s throughput-oriented model for schema-guided extraction from HTML. It is purpose-built for converting messy web pages into clean, typed JSON, rather than a general conversational assistant. The task has two semantic inputs: the HTML to inspect and a schema defining the information to extract. The result is a JSON object conforming to that structure.
The manufacturer’s workflow uses the schema instead of a natural-language user or system instruction as the extraction control. Put the field meaning in its description, with the appropriate type and any nested object or list structure. For a product page, a practical schema might request the product name, specification values with units and a list of supplied features. This example describes the desired record; the values should come from the page itself.
A clear field description distinguishes similar source values. Identify whether a field means the current product name, a technical identifier or a category, and explain how missing information should be represented. The maker demonstrates typed schema definitions and JSON-schema output, with validation of the returned object. Correct syntax does not itself establish that an extracted value agrees with the original page.
Inference.net recommends cleaning HTML to remove scripts, styles and boilerplate before extraction, using preprocessing that matches the training data. Trim to the relevant region where possible, while erring on the side of keeping more content when aggressive removal could discard useful evidence. Preserve headings, table relationships, labels and units needed to interpret a field.
For a page containing several products, make the desired item clear in the material and schema. Keep a source URL or page identifier in the surrounding task record so the extracted object can be checked later. Fetching the page is a separate operation performed by the application; Schematron extracts from the HTML it receives.
V2 Turbo is the version optimised for processing many pages with a defined output structure. V2 Small is the distinct extraction-quality option for complex schemas and long documents. Choose the workload role according to the actual records required, and evaluate how well the returned data preserves the information in representative pages. A high-throughput role does not make an unclear field definition precise.
The producer’s V2 family announcement documents a 128K context and strict JSON-mode schema adherence. HTML and the requested result share the available task budget. Large pages benefit from identifying the relevant content instead of using unrelated page structure as context.
The JSON output can feed a catalogue, ingestion task or a search system that needs specific facts instead of an entire raw page. Define the record once, compare extracted values with source examples and retain the source association. For a numeric field, check its units and whether the page describes a value, range or alternative configuration.
The V2 model remains separate from the original open-weight Schematron releases. Its documented extraction mode does not imply general chat, image analysis, autonomous browsing or tool execution. The useful deliverable is the requested typed record grounded in the supplied HTML, with unresolved or absent source information represented according to the schema.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits inference-net/schematron-v2-turbo | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00006 / 1K tokens Output · Output tokens $0.0003 / 1K tokens Input · Cache read $0.00006 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00006 / 1K tokens Output · Output tokens $0.0003 / 1K tokens Input · Cache read $0.00006 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "inference-net/schematron-v2-turbo", "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='inference-net/schematron-v2-turbo',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='inference-net/schematron-v2-turbo',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 inference-net/schematron-v2-turbo
Use the method paths shown above.
inference-net/schematron-v2-turboSchematron V2 Turbo turns supplied HTML into typed JSON using a caller-defined schema. The throughput-oriented V2 edition supports long-page extraction in a 128K producer context; field descriptions, nested objects and lists define the information to return.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Schematron V2 Turbo is Inference.net’s throughput-oriented model for schema-guided extraction from HTML. It is purpose-built for converting messy web pages into clean, typed JSON, rather than a general conversational assistant. The task has two semantic inputs: the HTML to inspect and a schema defining the information to extract. The result is a JSON object conforming to that structure.
The manufacturer’s workflow uses the schema instead of a natural-language user or system instruction as the extraction control. Put the field meaning in its description, with the appropriate type and any nested object or list structure. For a product page, a practical schema might request the product name, specification values with units and a list of supplied features. This example describes the desired record; the values should come from the page itself.
A clear field description distinguishes similar source values. Identify whether a field means the current product name, a technical identifier or a category, and explain how missing information should be represented. The maker demonstrates typed schema definitions and JSON-schema output, with validation of the returned object. Correct syntax does not itself establish that an extracted value agrees with the original page.
Inference.net recommends cleaning HTML to remove scripts, styles and boilerplate before extraction, using preprocessing that matches the training data. Trim to the relevant region where possible, while erring on the side of keeping more content when aggressive removal could discard useful evidence. Preserve headings, table relationships, labels and units needed to interpret a field.
For a page containing several products, make the desired item clear in the material and schema. Keep a source URL or page identifier in the surrounding task record so the extracted object can be checked later. Fetching the page is a separate operation performed by the application; Schematron extracts from the HTML it receives.
V2 Turbo is the version optimised for processing many pages with a defined output structure. V2 Small is the distinct extraction-quality option for complex schemas and long documents. Choose the workload role according to the actual records required, and evaluate how well the returned data preserves the information in representative pages. A high-throughput role does not make an unclear field definition precise.
The producer’s V2 family announcement documents a 128K context and strict JSON-mode schema adherence. HTML and the requested result share the available task budget. Large pages benefit from identifying the relevant content instead of using unrelated page structure as context.
The JSON output can feed a catalogue, ingestion task or a search system that needs specific facts instead of an entire raw page. Define the record once, compare extracted values with source examples and retain the source association. For a numeric field, check its units and whether the page describes a value, range or alternative configuration.
The V2 model remains separate from the original open-weight Schematron releases. Its documented extraction mode does not imply general chat, image analysis, autonomous browsing or tool execution. The useful deliverable is the requested typed record grounded in the supplied HTML, with unresolved or absent source information represented according to the schema.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits inference-net/schematron-v2-turbo | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00006 / 1K tokens Output · Output tokens $0.0003 / 1K tokens Input · Cache read $0.00006 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.00006 / 1K tokens Output · Output tokens $0.0003 / 1K tokens Input · Cache read $0.00006 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "inference-net/schematron-v2-turbo",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'inference-net/schematron-v2-turbo', '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': 'inference-net/schematron-v2-turbo', '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 inference-net/schematron-v2-turbo
Use the method paths shown above.
Technical example in English
Extract a typed product record\n\nHTML:\n<article><h1>Trail Pack</h1><dl><dt>Weight</dt><dd>1.2 kg</dd></dl><ul><li>Rain cover</li><li>Side pocket</li></ul></article>\nSchema:\n{"type":"object","properties":{"name":{"type":"string","description":"Product heading"},"weight_kg":{"type":"number","description":"Weight in kilograms"},"features":{"type":"array","items":{"type":"string"},"description":"Listed product features"}},"required":["name","weight_kg","features"],"additionalProperties":false}A JSON object with name as a string, weight_kg as a number and features as a list of strings, with no extra properties.Validate the requested schema and compare every value to the supplied HTML, including the kilogram unit.