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HubVibe Grounded Question Answering
x402
HubVibe Grounded Question Answering is a paid API for AI agents from hubvibe-io.com, paid per call via x402, $0.25/call, status unknown (last checked 2026-10-23).
Answers a specific question using only the text you supply, explicitly stating when the answer is not present in the material rather than hallucinating.
Price / call$0.25
Success—
StatusUnknown
How this service works
Question answering over your own text: ask a question about material you supply and get an answer grounded only in that text (Gemini). If the text does not contain the answer it says so instead of guessing. Use it for summaries, Q&A and checks over documents, emails and pages. Input: text, question; optional language. Fixed JSON fields: llm.extract.
Output
A natural language answer to the supplied question, derived strictly from the provided text. If the text does not contain sufficient information to answer the question, the response explicitly states that the material does not contain the answer rather than inferring or guessing.
Example use cases
Contract clause fact-checking
“Here's a section of a vendor contract: [paste text]. Does it specify a penalty for late delivery, and if so, what is it? Only answer based on what's actually written in the text.”
Support ticket triage question
“I have this customer email: 'I've been charged twice for order 8823 and nobody has responded to my last three emails.' Based only on that message, does the customer mention a preferred resolution method?”
Research article comprehension
“Here's an abstract from a research paper: [paste abstract]. What sample size did the researchers use in their study? If the abstract doesn't say, tell me it's not mentioned.”
Technical details
Endpoint
POSThttps://hubvibe-io.com/work/llm/analyze?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need answers strictly grounded in user-supplied text — especially for legal, compliance, or support contexts where hallucination is unacceptable. It is preferable over a generic LLM completion when you want an explicit 'not found' signal rather than a guess, and over a web search when the source of truth is your own document rather than the live web.
Side effects
- Empty or very short text input may yield a low-quality or empty answer
- Question is ambiguous and the model cannot determine what to answer
- Text is in a language the model handles poorly, reducing accuracy
- Very long text may be truncated depending on model context limits
- If no question is supplied, the response may default to a generic summary rather than targeted extraction
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| language | string | |
| question | string |
Output
Schema · Response
{
"type": "json",
"example": {
"result": {
"model": "gemini-2.5-flash",
"answer": "It sells machine-payable site audits at $0.05 per call.",
"tokens": {
"output": 88,
"prompt": 312
},
"question": "What does it sell, and at what price?",
"input_chars": 61
},
"status": "ok",
"worker": "llm.analyze",
"price_usd": 0.25,
"provenance": {
"steps": [
{
"ms": 640,
"ok": true,
"step": "analyze",
"provider": "gemini"
}
],
"attempts": 1,
"elapsed_ms": 660,
"providers_used": [
"gemini"
]
},
"receipt_id": "rcpt_9f1c2b3a4d5e6f70",
"receipt_url": "/work/receipts/rcpt_9f1c2b3a4d5e6f70"
}
}Tags
x402
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