import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "inference-net/schematron-v2-small", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "inference-net/schematron-v2-small", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"inference-net/schematron-v2-small","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
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Schematron V2 Small is an HTML-to-JSON extraction model from Inference.net that turns messy HTML into clean, schema-conforming JSON. It is built for structured extraction rather than general chat.
It takes raw HTML plus a JSON schema, such as Pydantic, Zod, or JSON Schema definitions. Extraction instructions are carried through the schema rather than through a system or user prompt.
It returns schema-conformant structured output in JSON. Inference.net describes the output as valid JSON by construction and strictly schema-compliant.
Yes, it supports structured output. The model is designed to return validated JSON that conforms to the provided schema.
Yes, the provider catalogue lists streaming as a capability for this model. The model is also described as having strict JSON mode for schema-compliant output.
It is targeted at web scraping, product data ingestion, financial document parsing, and search-augmented pipelines that need reliable structured output. Inference.net also describes it as useful for complex schemas and long pages.
Do not write a conversational prompt; send the schema and the HTML instead. The schema acts as the interface for extraction.
No. It is a specialized extraction model focused on HTML-to-JSON conversion and schema-constrained decoding.
It is intended for messy HTML and long pages that need clean typed JSON extraction. Inference.net says it delivers frontier-level extraction quality on complex schemas and long pages.
Developers usually look for whether they need reliable HTML-to-JSON extraction, schema-constrained output, and structured data pipelines. They also check that their workload fits a model built for extraction rather than open-ended generation.
