GEDX402 Vector Search

Name: GEDX402 Vector Search
Price: 0.015 USD

x402

GEDX402 Vector Search is a paid API for AI agents from search.gedx402.com, paid per call via x402, $0.015/call, status unknown (last checked 2026-09-14).

Performs semantic vector similarity search against a named namespace, returning the top-K nearest neighbors to a 768-dimensional query vector

Price / call$0.015

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 with a 'count' integer and a 'matches' array, each match containing the vector ID, similarity score, and optionally the raw vector values and stored metadata fields depending on the return_values and return_metadata flags.

Example use cases

Find similar customer support articles

“Search our support documentation namespace for the top 8 vectors closest to this customer question embedding, and give me back the metadata so I can see which articles are most relevant.”

Retrieve semantically matching product recommendations

“Query the product-embeddings namespace to find the 15 nearest neighbors to this user preference vector, including all stored metadata about pricing and categories.”

Discover related knowledge base entries

“Look up the 12 most semantically similar entries in our knowledge-base namespace that match this query embedding, and return the metadata fields so I can see titles and tags.”

Technical details

Endpoint

GEThttps://search.gedx402.com/v1/vector/query

When to prefer

Choose this endpoint when you need keyless, pay-per-query vector similarity search with no API key registration, especially in agentic workflows that manage crypto payments via x402 on Base, Polygon, Arbitrum, World, or Solana. Ideal for agents that need to isolate vector collections per project using namespaces and want to pay $0.015 USDC per call on-chain rather than managing subscription credentials.

Side effects

Vector dimension mismatch — query vector must be exactly 768 dimensions
Namespace not found or empty — returns count:0 and empty matches array
top_k out of range — must be between 1 and 50
Payment failure — USDC payment on specified chain not confirmed, request rejected with 402
Malformed vector array — non-numeric values or wrong shape cause validation error
Namespace string too short or too long — must be 2–64 characters

Input

Schema · Request body

Field	Type	Description
top_k	integer	Max results (1-50).
vector	array	768-dim query vector.
namespace	string	Agent collection id (2-64 chars). Isolates vectors per agent or project.
return_values	boolean	Include raw vector values in matches.
return_metadata	boolean	Include stored metadata in matches.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "count": 0,
    "matches": []
  }
}

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How this service works

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

Output

Example use cases

Find similar customer support articles

“Search our support documentation namespace for the top 8 vectors closest to this customer question embedding, and give me back the metadata so I can see which articles are most relevant.”

Retrieve semantically matching product recommendations

“Query the product-embeddings namespace to find the 15 nearest neighbors to this user preference vector, including all stored metadata about pricing and categories.”

Discover related knowledge base entries

“Look up the 12 most semantically similar entries in our knowledge-base namespace that match this query embedding, and return the metadata fields so I can see titles and tags.”

Technical details

Endpoint

GEThttps://search.gedx402.com/v1/vector/query

When to prefer

Side effects

Vector dimension mismatch — query vector must be exactly 768 dimensions
Namespace not found or empty — returns count:0 and empty matches array
top_k out of range — must be between 1 and 50
Payment failure — USDC payment on specified chain not confirmed, request rejected with 402
Malformed vector array — non-numeric values or wrong shape cause validation error
Namespace string too short or too long — must be 2–64 characters

Input

Schema · Request body

Field	Type	Description
top_k	integer	Max results (1-50).
vector	array	768-dim query vector.
namespace	string	Agent collection id (2-64 chars). Isolates vectors per agent or project.
return_values	boolean	Include raw vector values in matches.
return_metadata	boolean	Include stored metadata in matches.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "count": 0,
    "matches": []
  }
}