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Ephemeral RAG Store
x402
Ephemeral RAG Store is a paid API for AI agents from agent.halowerk.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-11).
Creates a temporary, isolated vector store (max 20 documents, 24-hour TTL) with local embedding and cosine-similarity retrieval via nomic-embed-text
Price / call$0.005
Success—
StatusUnknown
How this service works
Stores at most 20 short documents in an isolated SQLite row, embeds them with the local nomic-embed-text model, and performs cosine-similarity retrieval. Each store has a random identifier and a fixed 24-hour lifetime that is never extended by reads or writes. An in-process sweeper deletes expired rows every minute and each request also purges expiry. Input text is not written to request logs. This is retrieval infrastructure, not a durable database or an answer-generating model.
Output
Returns the store_id on creation, matched document chunks with cosine similarity scores on query, document counts and expiry timestamp on status, and confirmation on delete or upsert operations. No answer generation — raw retrieved passages only.
Example use cases
Temporary context store for multi-turn agent
“I need you to create an ephemeral store, load these 15 support articles into it, and then for each user question in this session retrieve the top 5 relevant chunks — the store only needs to last until this conversation is done.”
One-time document search over uploaded files
“Take these 8 documents I just uploaded, store them in a temporary vector store with a 24-hour lifetime, and search them for anything related to 'data retention compliance' — give me the top 3 matches.”
Disposable RAG pipeline for sensitive content
“I have some confidential text snippets I need to search semantically — create a short-lived store that won't persist in logs, add the 12 snippets, query it for 'authorization procedures', and delete the store when you're done.”
Technical details
Endpoint
POSThttps://agent.halowerk.com/v1/ephemeral-rag?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, privacy-conscious, short-lived semantic search layer for a small document set (up to 20 docs) that does not need to outlive a single session or task. Ideal for agentic pipelines that must avoid persisting sensitive text in logs or a durable database. Prefer over full vector databases when setup overhead, cost, and longevity are not warranted. Not appropriate when you need persistence beyond 24 hours, more than 20 documents, answer generation, or a durable knowledge base.
Side effects
- store_id not found or already expired (TTL elapsed after 24 hours)
- document limit exceeded (more than 20 documents submitted)
- text too long (document or query exceeds 5000 characters)
- invalid action enum value
- store_id missing or malformed on non-create actions
- embedding model unavailable or overloaded
- payment not completed (x402 payment required before processing)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | |
| top_k | integer | |
| action | string | |
| store_id | string | |
| documents | array |
Tags
x402
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