Vector Store Documentation
The Vector Store APIs allow you to create, manage, and search vector stores in Rememberizer. Vector stores enable you to store and retrieve documents using semantic similarity search.
Available Vector Store Endpoints
Management Endpoints
Get the information of a document
Document Operations
Upload files to a Vector Store
Update file's content in a Vector Store
Search Operations
Search for Vector Store documents by semantic similarity
Creating a Vector Store
To create a new Vector Store, use the following endpoint:
POST /api/v1/vector-stores/
Request Body
{
"name": "Store name",
"description": "Store description",
"embedding_model": "sentence-transformers/all-mpnet-base-v2",
"indexing_algorithm": "ivfflat",
"vector_dimension": 128,
"search_metric": "cosine_distance"
}
Response
{
"id": "store_id",
"name": "Vector Store Name",
"description": "Store description",
"created": "2023-05-01T00:00:00Z",
"modified": "2023-05-01T00:00:00Z"
}
Vector Store Configurations
To retrieve available configurations for vector stores, use:
GET /api/v1/vector-stores/configs
This will return available embedding models, indexing algorithms, and search metrics that can be used when creating or configuring vector stores.
Authentication
All Vector Store endpoints require authentication using either:
JWT token for management operations
API key for document and search operations
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