Document Ingestion Pipeline

Ingest PDFs, Word, PPTX, XLSX from Nextcloud/Paperless into Open WebUI's knowledge base and make them searchable via MCP.

Architecture

Nextcloud folder / Paperless webhook
  → n8n trigger (CT 104)
  → Docling MCP (port 18005) — PDF/DOCX/PPTX/XLSX → Markdown + structure
  → TEI /v1/embeddings  — multilingual-e5-base (local, 768d)
  → Qdrant (shared vector store, http://qdrant:6333)
  → Open WebUI knowledge base API  ← searchable in chat

Paperless shortcut: Paperless-ngx already OCRs documents. Its full-text content is available at /api/documents/?added__gt=<last_run>. An n8n workflow can re-embed directly from Paperless's REST API without re-running Docling for already-OCR'd PDFs.

Deployed components (as of 2026-05-26)

Component	Location	Endpoint
Qdrant	CT 104, `ai-internal` net	`http://qdrant:6333`
TEI (Text Embeddings Inference)	CT 104, `ai-internal` net	`http://tei:80`
Open WebUI	CT 104, port 14002	Uses Qdrant + TEI natively
Docling MCP	CT 104, port 18005	MCP server in gateway

Embedding stack

Model: intfloat/multilingual-e5-base (768d, multilingual DE+EN, ~278 MB ONNX)
Server: HuggingFace TEI cpu-1.6 — OpenAI-compatible at http://tei:80/v1/embeddings
Auth: none (internal network only)
Upgrade path: switch to mistral/mistral-embed via Bifrost (1024d, better quality) — requires re-index (drop + recreate Qdrant collections)

OWUI RAG config (env-driven)

VECTOR_DB=qdrant
QDRANT_URI=http://qdrant:6333
RAG_EMBEDDING_ENGINE=openai
RAG_OPENAI_API_BASE_URL=http://tei:80
RAG_OPENAI_API_KEY=none
RAG_EMBEDDING_MODEL=intfloat/multilingual-e5-base
CONTENT_EXTRACTION_ENGINE=docling
CHUNK_SIZE=1200
CHUNK_OVERLAP=150
ENABLE_RAG_HYBRID_SEARCH=true

Bifrost embedding models (available for external services / upgrade)

Three embedding models tested and working via http://bifrost:8080/v1/embeddings:

mistral/mistral-embed (1024d) — ✓ production-ready
mistral/codestral-embed (1024d) — ✓
gemini/gemini-embedding-001 (768d/1536d) — ✓

Converter comparison

Tool	Image	Formats	Notes
Docling (deployed)	MCP on 18005	PDF, DOCX, PPTX, XLSX, HTML	Best for structured Office/PDF with tables
MinerU	`opendatalab/mineru`	PDF (layout-aware, OCR)	Better for academic papers / scanned PDFs
Markitdown (in gateway)	—	Office, PDF	Ad-hoc only; not suitable for batch

Start with Docling — already deployed. Add MinerU if academic paper OCR quality is needed.

Qdrant is on ai-internal network — any service on that network can use it:

from qdrant_client import QdrantClient
client = QdrantClient(url="http://qdrant:6333")

n8n, MCP tools, and custom pipelines should use http://tei:80/v1/embeddings for consistent 768d vectors. Mixing models/dimensions in the same collection will fail.

Intel Arc GPU passthrough (planned)

CT 104 has Intel Core Ultra 7 155H iGPU but no /dev/dri/render* device is passed through. When passthrough is enabled, switch TEI to the Intel image:

image: ghcr.io/huggingface/text-embeddings-inference:intel-1.6

This uses IPEX and runs ~5–10× faster for embedding batches.

Pending

n8n workflow: Nextcloud/Paperless → Docling → TEI → Qdrant
MCP knowledge-search tool pointing at Qdrant (replaces LobeChat search intent)

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