Platform
Private AI infrastructure — from compute to agents.
Target groups
For enterprise, SMBs, and individual developers.
Knowledge & Support
Everything you need to succeed with Mycelis.
Intelligence
Upload documents, and Mycelis automatically creates vectors. On every request, relevant content is injected as context — without your own vector database or embedding pipeline.
What is RAG?
RAG (Retrieval-Augmented Generation) is a technique where the model does not answer from memory alone. It first retrieves relevant documents from a database and uses them as context.
Result: the model responds based on your latest documents — fewer hallucinations from outdated information and no base-model retraining required.
Supported file formats
Text, tables
TXT / MD
Plain text, Markdown
DOCX
Word documents
HTML / JSON
Structured content
Automatic embedding pipeline
PDF, TXT, DOCX, or Markdown. Maximum file size: 50 MB per file, 500 MB per knowledge base.
Mycelis automatically splits the document into semantic chunks. Default: 512 tokens with 50-token overlap.
Each chunk is converted into a 1536-dimensional vector using text-embedding-3-small.
Vectors are stored in a dedicated Qdrant collection — isolated per workspace.
On each model request, the query is vectorized, similar chunks are retrieved, and inserted as context.
Frequently asked questions
No vector database to run, no embedding code to maintain. Just upload and go.
Start for free