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Vector Namespace Semantic Search
x402
Vector Namespace Semantic Search is a paid API for AI agents from pearl-valium-stickers-upc.trycloudflare.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-31).
Embeds a query using the same model used at index time and returns ranked semantic search hits with scores, text, and metadata from a private vector collection.
Price / call$0.01
Success—
StatusUnknown
How this service works
Search a namespace you indexed: the query is embedded on the same model and matched against your collection. Returns hits with score, text and metadata, plus latency.
Output
Returns an array of ranked hits, each containing a relevance/similarity score (cosine), the matched document text, and associated metadata. Also includes query latency in milliseconds.
Example use cases
Knowledge base Q&A retrieval
“Search my 'internal-kb' namespace for documents related to 'onboarding new employees' and show me the top 3 results with their relevance scores.”
RAG pipeline document lookup
“I'm building a RAG chatbot — query my 'legal-contracts' namespace with the question 'what are the termination clauses?' and give me the top 5 matching passages with metadata so I can feed them to the LLM.”
Support ticket context matching
“Search my 'support-history' vector collection for past tickets similar to 'customer can't log in after password reset' and return the top 4 hits with scores and any attached metadata.”
Technical details
Endpoint
POSThttps://pearl-valium-stickers-upc.trycloudflare.com/v1/search?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have already indexed documents into a private vector namespace on this service and need to perform semantic similarity search without sending data to a third-party cloud. Prefer it over generic vector DBs (Pinecone, Weaviate) when privacy is a concern since everything runs locally on the operator's hardware. It pairs naturally with the sibling index endpoint on the same host.
Side effects
- Namespace not found — collection was never indexed or wrong name provided
- Query embedding fails if the model is unavailable or overloaded on the local GPU
- Empty results if the namespace has no documents or the query has no semantic overlap
- Malformed input schema causes a 4xx error if 'hits' array or query text is missing
- Slow response or timeout if the RTX 5090 is busy with a concurrent heavy task
- Payment failure via x402 if USDC balance is insufficient or wallet is misconfigured
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| hits | array |
Tags
x402
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