VectorsDB_
Store vector embeddings and run similarity search with Appwrite VectorsDB to power semantic search, recommendations, and other AI features.
2 min read
Appwrite VectorsDB lets you store vector embeddings and run similarity search over them. A collection is created with a fixed dimension, every document holds an embeddings vector of that length plus optional metadata, and an HNSW index keeps similarity search fast as your data grows.
Databases store data, if you need to store files like images, PDFs or videos, use Appwrite Storage.
You organize data into databases, collections, and documents, the same way you do across Appwrite Databases. What sets VectorsDB apart is the fixed schema built for vectors and the ability to generate text embeddings and search by similarity.
Key concepts
Databases
Create and manage vector databases, the largest organizational unit.
Collections
Created with a required dimension, the length of the vectors they hold, plus an optional metadata object per document.
Documents
Store one embedding under embeddings and any associated JSON under metadata.
Embeddings
Generate embeddings from text with built-in models, without a separate embedding service.
Permissions
Control access at the collection and document level.
Queries
Filter documents by their metadata with comparison, string, and logical operators.
Order
Sort results by metadata fields.
Backups
Schedule backup policies and restore from any backup.
Guides
Vector search
Create an HNSW index on embeddings, then rank documents by cosine, dot product, or Euclidean distance.
Pagination
Page through results with offset or cursor pagination.
Transactions
Stage operations and commit or roll them back atomically.
Bulk operations
Create, update, upsert, or delete many documents in one request.
Timestamp overrides
Set custom creation and update timestamps during imports.
CSV imports
Import documents from CSV files.
CSV exports
Export collections to CSV files.
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