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gate402 Semantic Vector Cache (Dedup)
x402
gate402 Semantic Vector Cache (Dedup) is a paid API for AI agents from gate402.app, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-14).
Checks whether a query has been seen before using exact-match lookup then 0.88 cosine similarity on hash embeddings, returning a cache hit or miss in under 10ms.
Price / call$0.003
Success—
StatusUnknown
How this service works
Semantic vector cache: exact-match Map shortcut, then 0.88 cosine similarity on hash embeddings. Sub-10ms hits.
Output
Returns a cache hit or miss result: on a hit, includes the matched query text and cosine similarity score (>= 0.88); on a miss, optionally stores the provided embedding vector for future lookups. Sub-10ms latency for hits.
Example use cases
LLM cost reduction in chatbot
“Before sending this user message to GPT, check the cache in namespace 'support-bot' to see if a semantically similar question was already answered — if it's a miss, store the embedding so future hits are fast.”
Deduplicating agent task queue
“I'm running a task queue where agents submit queries like 'summarize Q3 earnings' — check if this exact or near-identical task already exists in the 'task-dedup' namespace before spawning a new agent.”
Preventing duplicate form submissions
“A user just submitted the feedback 'Your app crashes on login every time' — check the 'feedback' namespace to see if we've already captured a semantically identical complaint and skip storing it if similarity is above threshold.”
Technical details
Endpoint
POSThttps://gate402.app/v1/dedupWhen to prefer
Choose this endpoint when you need sub-10ms semantic deduplication of text queries before invoking expensive downstream APIs or LLMs. Ideal for agent pipelines that need to short-circuit repeated or paraphrased requests without a full vector database. Prefer this over full vector DB queries when you only need a hit/miss signal with optional storage, and latency is critical.
Side effects
- Missing 'query' field returns a 400 validation error
- Namespace not found may return empty results or auto-create namespace
- Vector dimension mismatch if provided embedding doesn't match stored dimension
- Similarity threshold not met returns a miss even for close paraphrases
- storeOnMiss=true without providing a vector may fail or be ignored
- Rate limit exceeded returns 429
- Payment failure via x402 returns 402
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | Text to look up |
| vector | array | Optional embedding to store on miss |
| namespace | string | |
| storeOnMiss | boolean |
Tags
x402
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