GEDX402 AI Search – RAG Answer from URLs is a paid API for AI agents from rag.gedx402.com, paid per call via x402, $0.625/call, status unknown (last checked 2026-09-14).
Fetches and indexes one or more public URLs, then answers a natural-language question using retrieved content chunks, returning a cited answer and a session for follow-up queries.
Single-hop cited RAG answer — one x402 POST indexes URL(s), hybrid retrieval, and chat/fast synthesis with session_token for follow-up queries. $0.625 USDC per request. Agent search: rag answer, research, cited answer, hybrid retrieval.
A JSON object containing a natural-language answer with inline citation numbers, an array of citations (URL, snippet, relevance score), a session token and query path for follow-up questions without additional payment, remaining query and index-op credit counts, and provenance data including fetch timestamp and content hash.
GEThttps://rag.gedx402.com/v1/aisearch/outcome/answerChoose this endpoint when you need to answer natural-language questions grounded in specific public web content (up to 5 URLs) and want cited, retrievable answers without managing API keys or vector database infrastructure. It is ideal for agents that need to do lightweight RAG over fresh URLs on demand, paid per-call via USDC on multiple chains. Prefer it over general web search when you have specific source URLs and need traceable citations.
| Field | Type | Description |
|---|---|---|
| url | string | |
| poll | boolean | |
| urls | array | |
| top_k | integer | |
| budget | object | |
| rerank | boolean | |
| question | string | |
| rewrite_query | boolean | |
| retrieval_type | string |
{
"type": "json",
"example": {
"answer": "Configure AI Search by … [1]",
"credits": {
"queries_remaining": 4,
"index_ops_remaining": 0
},
"citations": [
{
"url": "https://example.com/docs",
"index": 1,
"score": 0.92,
"snippet": "…"
}
],
"provenance": {
"fetched_at": "2026-06-18T12:00:00.000Z",
"latency_ms": 2400,
"content_sha256": "a1b2c3d4e5f6789012345678901234567890abcd"
},
"query_path": "/v1/aisearch/ged-abc123/query",
"session_id": "550e8400-e29b-41d4-a716-446655440000",
"instance_id": "ged-abc123",
"instructions": "Send session_token via X-AISearch-Session-Token header or body on query_path — no x402 until credits exhausted.",
"session_token": "abc123…"
}
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