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Vector Memory
Long-term memory & RAG in one call
📚 Knowledge & RAGAPIMCPSDK
Overview
Embed, store and semantically search documents without managing a vector database. Chunking, embeddings and reranking are handled for you — just upsert and query.
Call it from any agent
curl https://api.agishub.com/v1/tools/memory \
-H "Authorization: Bearer $AGISHUB_KEY" \
-H "Content-Type: application/json" \
-d '{ "action": "query", "query": "refund policy", "top_k": 4 }'Parameters
| Name | Type | Required | Description |
|---|---|---|---|
| action | string | Yes | upsert | query. |
| text | string | No | Content to embed (upsert). |
| query | string | No | Semantic query. |