Knowledge rack (RAG)
Upload your own documents and have answers backed by them — tenant-isolated retrieval-augmented generation on EU infrastructure.
The knowledge rack is an organisation's searchable document collection. Technically it is RAG (retrieval-augmented generation): before answering, SovrGPT searches your own documents, hands the matching passages to the model and backs the answer with sources. The metaphor: a "rack" your team puts its documents on so the model can reach them.
What it can do
- Your own documents as a knowledge source: PDF, Word, Excel, text, Markdown.
- Sourced answers: passages are cited — traceable, which matters for public administration, legal and compliance.
- Controllable per chat: you decide per conversation whether the rack is used, only the rack, only the web, or a combination.
- Strictly tenant-separated: one organisation never sees another's knowledge.
Choosing knowledge sources per chat
You choose the knowledge source in the composer (the RAG: pill). There are five
modes:
| Mode | Behaviour |
|---|---|
| Off | No knowledge rack — a normal chat. |
| Knowledge rack | Enrich answers with your knowledge rack. |
| Knowledge rack only | Answer exclusively from the rack (no world knowledge). |
| Web | Web search only, without the knowledge rack. |
| Rack + web | Combine the knowledge rack and web search. |
The organisation sets a default mode; every chat can override it. "Knowledge rack only" is ideal when answers must rest exclusively on approved in-house documents.
Managing documents
Under Settings → Knowledge (RAG):
- Upload files (PDF/Word/Excel/text/Markdown). The text is extracted, split into sections, turned into vectors and stored.
- Review the document list, delete individual documents.
- Keep an eye on the quota (number of documents and total size).
Content is stored encrypted in the EU and stays confined to that organisation.
How the search works
SovrGPT combines two methods and then re-orders the hits:
- Hybrid search — keyword search (Postgres full text) and semantic vector search (pgvector) are merged.
- Reranking — a cross-encoder (
bge-reranker-base) sorts the best hits by actual relevance. - Injection + citation — the top passages go to the model, and the answer points back to where they came from.
Embedding and reranking run on EU-sovereign endpoints — no hyperscaler, no data outside the EU.
Tenant isolation (non-negotiable)
Strict separation per organisation is the core requirement of this feature:
org_idon every RAG table — no record without an organisation.- Row-level security: reading only for members of the organisation; writing exclusively server-side (never directly from the client).
- Encrypted storage in EU data centres.
Availability (plans)
| Feature | From plan |
|---|---|
| Upload to and query the knowledge rack (RAG) | Pro |
| Web search as a source | all plans |
If your tier is not sufficient, the RAG: pill shows an upgrade hint.
Rack vs. file attachment
- Knowledge rack = the organisation's persistent knowledge base, searchable across all chats.
- File attachment in a chat = transient: a file is only taken into account for the ongoing conversation and is not indexed permanently.
Related topics: Marketplace · Connecting your own systems · GDPR & compliance.