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Document parsing/indexing and online queries have different resource profiles.
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In RAG, the expensive failure is often not the model but parsing. If PDF tables, OCR, chunking, embeddings or retrieval are wrong, a strong LLM still receives poor context. Test installation through the document pipeline.
Do not count “PDF uploaded” as success. Validate tables, headings, footnotes and language text from sampled pages through retrieval before trusting ingestion quality.
For RAGFlow, ingestion workload matters as much as baseline CPU/RAM. Parsing hundreds of PDFs can have a very different resource profile from steady-state chat queries; benchmark indexing and querying separately.
For RAGFlow, ingestion workload matters as much as baseline CPU/RAM. Parsing hundreds of PDFs can have a very different resource profile from steady-state chat queries; benchmark indexing and querying separately.
Deploy RAGFlow on a VPS for production with Docker host sizing, document parsing, storage, search/vector layers, model endpoints, backups and ingestion validation.
Document parsing/indexing and online queries have different resource profiles.
Poor chunk boundaries degrade retrieval before model quality matters.
RAG answers should be traceable back to source chunks.
Protect raw source documents as well as indexes/vector data.
Observe upload, parse, chunk, embed, retrieve and generate separately to avoid vague diagnoses such as “the model hallucinated.”
| Stage | Test |
|---|---|
| Parse | Is text/table correct? |
| Chunk | Is context split badly? |
| Embed | Were vectors created? |
| Retrieve | Expected chunk in top-k? |
| Generate | Does answer follow context? |
OCR/parsing and embedding across hundreds of documents can spike CPU/RAM/I/O. Avoid scheduling bulk ingestion during peak query traffic.
nprocfree -hdf -hTiostat -xz 1 5 2>/dev/null || truedocker stats --no-streamAn index may be rebuildable, but not if raw source documents are lost. Their retention and backup value differ.
A chat model change is different from changing embedding model/dimensions, which can require full re-indexing. Treat embedding changes as migrations.
| Component | Change impact |
|---|---|
| Chat LLM | Answer style/reasoning |
| Embedding model | Index compatibility/rebuild |
| Reranker | Top-k ordering |
For each question, record expected source document/page/chunk and measure retrieval hit rate before judging generation.
Even with restored database/object storage, search/vector version mismatch can break queries. Run a sample of the golden set after recovery.
docker compose psdocker compose logs --tail 120 | grep -Ei "error|fail|oom" || truedf -hTHiding a folder in the UI is not authorization. Tenant/department/user scope must be enforced server-side in retrieval.
Plan RAGFlow parsing, vector/search and model inference on one or separate Eka Sunucu VPS/GPU nodes according to workload.
Primary documentation and technical references used by this guide.
Continue with related infrastructure and implementation guides.
RAGFlow VPS
Platform/parsing can run on CPU for some workloads; local embedding/LLM/OCR choices determine GPU need. With remote model APIs, GPU may live elsewhere.
No. Validate parsed text, tables, chunks and retrieval on sampled pages.
Vector dimensions/semantic space may change; compatibility with existing indexes is not guaranteed and re-indexing may be required.
No. RAG retrieves external context at query time and does not require changing model weights.