Skip to main content

​
Build collections

  • Create: Click New Collection, name it, provide a source (bucket or collection), and select feature extractors. API: Create Collection.
  • Features: Studio shows available feature addresses and vector indexes. Use this to configure retrievers. API: Describe Features.
  • Update: Modify description, enablement, and taxonomy applications. API: Update Collection.
  • List/Get/Delete: Browse, inspect, or remove collections. API: List, Get, Delete.

​
Documents

  • View documents produced by extraction with metadata and vectors.
  • Create or update documents directly for advanced workflows. API: Create, Update, Get, List, Delete.

​
Tips

  • Collections are the searchable view of your data; choose extractors that match your modalities.
  • Ensure internal_id and X-Namespace context is correct—Studio handles this automatically.
1

Create a collection

Select a source, add feature extractors, and save. Studio bootstraps vector indexes as needed.
2

Inspect features

Use Describe Features to confirm vector names, dimensions, and index metadata for retriever setup.
3

Attach taxonomies (optional)

Configure on-demand or materialized applications from the collection page.
4

Attach retriever enrichments (optional)

Configure retriever enrichments to run retriever pipelines on documents during post-processing. Each enrichment maps document fields to retriever inputs, executes the retriever, and writes selected result fields back to the document. Use for LLM classification, cross-collection joins, or multi-stage enrichment at ingestion time.

​
Storage Tab

The Storage tab on each collection page lets you manage the storage lifecycle:
  • View lifecycle state: Active, Cold, or Archived with vector counts
  • Transition tiers: Move to Cold (evict from Qdrant), Rehydrate (restore to Qdrant), or Archive (permanent deletion)
  • Configure tiering rules: Set auto-evict and auto-archive thresholds (stored for future enforcement)
  • Storage breakdown: See vector counts in Qdrant vs S3 Vectors
See Storage Tiering for full documentation.
Documents are stored as Qdrant points with payload and vectors; the point ID is the document_id in results.