AI Data Storage
Store, manage, and index documents with chunking and embedding for semantic search.
Purpose
AI Data Storage (RAG) is a component of the Latenode platform designed for storing and indexing text files, images, and other knowledge sources.
🧠 This tool is primarily intended to be used in conjunction with the AI Agent — it provides documents in the form of chunks, which the agent can then use to generate responses.
Use cases include:
- Uploading and storing structured or unstructured content
- Generating embedding vectors for fast semantic search
- Running natural language search queries
- Connecting to the RAG Search node inside a scenario
How to Access
You can access this feature via Data Storage → AI Data Storage (RAG) in the left-hand side menu.
Creating Storage
Click Create Storage to open the setup modal:
Fill in the required fields: Storage Name, Chunk Size, Chunk Overlap
What are Chunk Size and Overlap?
- Chunk Size — the number of tokens in a single chunk. Smaller chunks provide higher accuracy but increase the total number of chunks.
- Chunk Overlap — the percentage of token overlap between neighboring chunks. Helps maintain context across them.
Managing Storage
Created storages are displayed in a table:
Field | Description |
Name | Storage name |
Chunk Size | Number of tokens per chunk |
Chunk Overlap | Overlap between chunks in % |
Created | Creation date |
Updated | Last updated date |
Uploading Files
Open a storage to access the upload interface. Drag-and-drop is supported.
After uploading:
- Each file is processed and indexed (status: Processing)
- Files are listed with size, upload date, and status
- Editing or downloading files is currently not supported
Features & Limits
Feature | Status |
OCR | Supported (English and Russian) |
Image Upload | Supported (if image contains text) |
File Editing | Not supported |
File Download | Not yet available |
Automatic Indexing | Yes |
Supported Formats | PDF, TXT, JSON, MD, PNG, JPG, and more |
Upload via scenario | Not yet supported |
Technical Details
Parameter | Value |
Max file size | 20 MB (50 MB planned) |
Embedding model | Cloudflare + LlamaIndex |
Vector limit | 5 000 000 vectors per account |
Billing | Charged only during file upload (PNP credits) |
Billing
- PNP credits are deducted upon file upload
- Billing is based on pages/chunks
- Example: 1 page ≈ 6600 microcredits
- Queries via RAG Search are not additionally billed
🧪 RAG is currently in beta. Pricing, behavior, and limitations may change.
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