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layout page
title Collections
description Group documents into indexable collections for RAG workflows in NodeTool.

Collections bundle related documents into a single searchable unit. Connect a collection to an index node and any file in it — PDFs, Markdown notes, HTML, transcripts — becomes queryable from your workflows.

Collections Explorer

Opening Collections

Open Collections from the app menu, or go directly to /collections. The explorer shows every collection you've created with its document count, embedding model, and ingestion workflow.

Creating a Collection

  1. Click Create Collection.
  2. Give it a name.
  3. Choose an embedding model. Both fields are required.
  4. Drag files from your computer onto the collection tile to index them.

New Collection

Collections accept any file type, but only text-extractable formats are indexed:

Format Extracted
PDF Text + layout (per page)
DOCX / DOC Body text
Markdown / TXT Raw text
HTML Stripped body text
CSV / TSV Rows as records
EPUB Chapter text

Unsupported formats are stored but not indexed — still handy for reference inside a collection.

Managing Documents

Each collection is a tile in the explorer. From the tile you can:

  • Index documents by dropping files onto it. Progress and any per-file errors are reported inline.
  • Change the ingestion workflow that processes dropped files.
  • Delete the collection and everything indexed in it.

Collection Details

Using Collections in Workflows

Collections shine in RAG pipelines:

  1. Add an index node (vector.IndexString, vector.IndexTextChunk, vector.IndexAggregatedText, vector.IndexImage, vector.IndexEmbedding) or a query node (vector.QueryText, vector.QueryImage, vector.HybridSearch) to your workflow.
  2. Connect the collection to its collection input — the node menu will suggest the selector.
  3. Run the workflow. Index nodes write into the collection; query nodes read from it.

See the [Chat with Docs example]({{ '/workflows/chat-with-docs' | relative_url }}) for the full query → format → answer wiring.

Indexing Options

A collection's embedding model is chosen when you create it and is recorded on the collection. Its ingestion workflow — the workflow that runs over each dropped file — can be changed from the collection tile.

Chunking is the ingestion workflow's business, not a collection setting. The default splitter chunks at 2000 characters with 1000 characters of overlap.

Collection Settings

See [Indexing]({{ '/indexing' | relative_url }}) for deeper tuning notes.

Storage

Index data is stored alongside your workflow database in SQLite (via sqlite-vec). Nothing leaves your machine unless you've opted into a cloud provider for the embedding model.

For multi-user deployments, Supabase-backed collections are an option — see [Supabase Deployment]({{ '/supabase-deployment' | relative_url }}).

Related Docs

  • [Indexing]({{ '/indexing' | relative_url }}) — advanced chunking, hybrid search, maintenance
  • [Asset Management]({{ '/asset-management' | relative_url }}) — add documents to the underlying asset library
  • [Chat with Docs example]({{ '/workflows/chat-with-docs' | relative_url }}) — end-to-end RAG workflow