GEDX402 Vector Upsert

x402

GEDX402 Vector Upsert is a paid API for AI agents from search.gedx402.com, paid per call via x402, $0.025/call, status unknown (last checked 2026-09-15).

Embeds and stores a document (text + metadata) into an isolated vector namespace for later semantic search, paid per call via USDC on-chain.

Price / call$0.025

Seller volume →—

Success—

StatusUnknown

How this service works

x402 workers ai. pay with usdc on base, polygon, arbitrum, world, or solana. no api keys.

Output

A JSON object confirming the upsert with the document's stable id (e.g. 'doc-1') and a mutationId string that can be used to track or verify the write operation.

Example use cases

Example prompt

“Store this product description in my 'product-catalog' namespace with id 'prod-9921': 'The AeroGlide X1 is a lightweight carbon-fiber drone with 40-minute flight time, 4K camera, and obstacle avoidance — ideal for professional aerial photography.'”

Technical details

Endpoint

GEThttps://search.gedx402.com/v1/search/upsert

When to prefer

Choose this endpoint when you need to add or update a single document in an isolated vector namespace using crypto micropayments with no API key setup. Ideal for AI agents building or maintaining a semantic memory layer, knowledge base, or retrieval-augmented generation (RAG) pipeline, especially when operating in a permissionless, keyless environment funded by on-chain USDC (Base, Polygon, Arbitrum, World, or Solana).

Side effects

Missing required 'text' field — document cannot be embedded without content
Invalid or missing 'namespace' (must be 2–64 characters) — request rejected
Duplicate 'id' within namespace — overwrites existing document silently (idempotent)
Payment failure or insufficient USDC balance — 402 response, request not processed
Malformed metadata object — may cause schema validation error
Namespace not found or permission mismatch — vector isolation enforced per agent

Input

Schema · Request body

Field	Type	Description
id	string	Stable document id within the namespace.
text	string	Document text to embed and store.
metadata	object	Optional metadata stored with the vector.
namespace	string	Agent collection id (2-64 chars). Isolates vectors per agent or project.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "id": "doc-1",
    "mutationId": "…"
  }
}

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Technical details

Endpoint

GEThttps://search.gedx402.com/v1/search/upsert

When to prefer

Side effects

Missing required 'text' field — document cannot be embedded without content
Invalid or missing 'namespace' (must be 2–64 characters) — request rejected
Duplicate 'id' within namespace — overwrites existing document silently (idempotent)
Payment failure or insufficient USDC balance — 402 response, request not processed
Malformed metadata object — may cause schema validation error
Namespace not found or permission mismatch — vector isolation enforced per agent

Input

Schema · Request body

Field	Type	Description
id	string	Stable document id within the namespace.
text	string	Document text to embed and store.
metadata	object	Optional metadata stored with the vector.
namespace	string	Agent collection id (2-64 chars). Isolates vectors per agent or project.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "id": "doc-1",
    "mutationId": "…"
  }
}