CreateVectorStoreRequest

Request body for creating a vector store.

Example Usage

import { CreateVectorStoreRequest } from "@meetkai/mka1/models/components";

let value: CreateVectorStoreRequest = {
  name: "Product Documentation",
  description: "Vector store for product manuals and documentation",
  expiresAfter: {
    anchor: "last_active_at",
    days: 30,
  },
};

Fields

Field	Type	Required	Description
name	string	➖	The name of the vector store.
description	string	➖	A description for the vector store. Can be used to describe the vector store's purpose.
fileIds	string[]	➖	A list of File IDs that the vector store should use. Useful for tools like file_search that can access files. At most 500 per request.
expiresAfter	components.ExpiresAfter	➖	The expiration policy for a vector store.
chunkingStrategy	components.CreateVectorStoreRequestChunkingStrategy	➖	The chunking strategy used to chunk the file(s). If not set, will use the auto strategy. Only applicable if file_ids is non-empty.
metadata	Record<string, string>	➖	Set of 16 key-value pairs that can be attached to an object. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.
embeddingModel	string	➖	The embedding model to use. Defaults to the auto-configured model if not specified.
embeddingDimensions	number	➖	The number of dimensions for the embedding vectors. Only supported for models with flexible dimensions. If not specified, uses the model's default dimensions.
retrievalMode	components.RetrievalMode	➖	Retrieval mode, frozen at creation (cannot be changed later). 'vector' (default): standard vector similarity search. 'graph': GraphRAG — entities and relations are extracted from every chunk at ingest (metered against your usage) and search traverses the knowledge graph.
extractionModel	string	➖	Model used for entity/relation extraction on graph stores (ingest-time triplet extraction and query-entity extraction). Defaults to the auto-configured model, resolved at creation time — same contract as embedding_model. Only valid when retrieval_mode is 'graph'.
maxHops	number	➖	Graph expansion depth for graph-mode queries (1-4, engine default 2). Only valid for graph stores.