HubVibe Named Field Extractor

x402

HubVibe Named Field Extractor is a paid API for AI agents from hubvibe-io.com, paid per call via x402, $0.25/call, status unknown (last checked 2026-10-27).

Extracts named fields from free-form text and returns them as a JSON object with exactly the requested keys, returning null for absent fields rather than hallucinating values.

Price / call$0.25

Seller volume →—

Success—

StatusUnknown

How this service works

Structured data extraction / text to JSON: pull the fields you name out of any text into a JSON object with exactly those keys, null where the text does not state a value (Gemini, no invented values). Use it to turn emails, invoices, pages or documents into clean records. Input: text, fields[]. From a live web page: research.page_facts.

Output

A JSON object containing exactly the field names the caller requested as keys. Each key maps to the extracted string value found in the input text, or null if the text did not explicitly state a value for that field. No fields are fabricated or inferred beyond what the text states.

Example use cases

Extract contact details from bio

“Here's a speaker bio from our event website — pull out their name, email, phone number, and company as JSON. If any of those aren't in the text, return null: 'Jane Doe is a product lead at Acme Corp. She can be reached at jane@acme.com or via her mobile 555-123-4567.'”

Parse invoice fields from email body

“From this email body, extract the invoice number, due date, total amount, and vendor name as a structured JSON object — anything not explicitly stated should come back as null.”

Pull product specs from listing text

“Take this product description and give me the brand, model number, weight, dimensions, and price as JSON keys — return null for any spec that isn't mentioned in the text: 'The UltraSound Pro X2 by SonicBrand weighs 1.2kg and is priced at $299. Dimensions not listed.'”

Technical details

Endpoint

POSThttps://hubvibe-io.com/work/llm/extract?utm_source=zero.xyz

When to prefer

Choose this endpoint when you have free-form text and a known set of field names you want to extract without hallucination — especially when null for absent fields is preferable to invented data. Ideal for parsing documents, emails, listings, bios, or any unstructured content into a structured JSON shape with exact key control. Prefer over general LLM prompting when you need guaranteed key presence and explicit null semantics.

Side effects

Text does not contain any of the requested fields — all values return as null
Fields array is empty — returns empty JSON object
Text is too long or malformed — may return partial extraction or error
Ambiguous text causes incorrect field assignment to wrong key
Non-English text may reduce extraction accuracy

Input

Schema · Request body

Field	Type	Description
text	string
fields	array
language	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": {
      "model": "gemini-2.5-flash",
      "fields": {
        "title": "HubVibe",
        "pricing": "$0.05 per call"
      },
      "tokens": {
        "output": 88,
        "prompt": 312
      }
    },
    "status": "ok",
    "worker": "llm.extract",
    "price_usd": 0.25,
    "provenance": {
      "steps": [
        {
          "ms": 640,
          "ok": true,
          "step": "extract",
          "provider": "gemini"
        }
      ],
      "attempts": 1,
      "elapsed_ms": 660,
      "providers_used": [
        "gemini"
      ]
    },
    "receipt_id": "rcpt_9f1c2b3a4d5e6f70",
    "receipt_url": "/work/receipts/rcpt_9f1c2b3a4d5e6f70"
  }
}

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