# Ingest Pipeline — Filesystem Scan

One-shot pipeline that crawls a directory, converts documents to text, embeds them via
the nomic service, and stores them in Qdrant. Run it manually against any mounted path.

## Architecture

```
 ┌─────────────────────────┐
 │  ingest.py --path /...  │  runs on CT 104 (has direct access to ai-internal network)
 └──────────┬──────────────┘
            │ per file
            ▼
 ┌────────────────────────────────────────────────────────────────┐
 │  1. File scanner          glob recursively, filter by ext      │
 │  2. Change detection      sha256(path + mtime) → skip if seen  │
 │  3. Docling converter     POST http://docling:5001/convert      │
 │     (PDF/DOCX/PPTX/XLSX/HTML → Markdown)                       │
 │     Plain text/Markdown   read directly                        │
 │     Images (jpg/png/...)  send to nomic vision endpoint        │
 │  4. Chunker               split Markdown by headers + size     │
 │     chunk_size=1200  overlap=150  (matches OWUI RAG config)    │
 │  5. Nomic embedder        POST http://nomic:80/v1/embeddings   │
 │     text chunks → 768d   images → 768d (same space!)          │
 │  6. Qdrant upsert         collection `documents`, named vectors │
 └────────────────────────────────────────────────────────────────┘
            │
            ▼
 ┌──────────────────────────────────────────────────────────────┐
 │  Qdrant: http://qdrant:6333                                  │
 │  collection: documents                                       │
 │  vector: {size: 768, distance: Cosine}                       │
 │  payload: {path, title, chunk_idx, text, type, mtime, hash} │
 └──────────────────────────────────────────────────────────────┘
            │
            ▼
 ┌────────────────────────────┐
 │  Open WebUI RAG search     │  queries `documents` collection
 │  n8n / MCP tools           │  same collection, same embedding space
 └────────────────────────────┘
```

## Why not n8n for this

n8n is the right tool for **event-driven** pipelines (webhook on new file, Paperless
webhook, Nextcloud activity, scheduled re-sync). For a **bulk one-shot crawl**, a Python
script is better:
- direct filesystem access with `os.walk`
- proper progress bar and error recovery
- batched Qdrant upserts (n8n does one HTTP call per node)
- easy CLI: `python3 ingest.py /mnt/pve/unas/Notizen`

n8n handles the _ongoing_ layer (see [Ongoing ingestion](#ongoing-ingestion-n8n)).

## Supported file types

| Extension | Handler | Notes |
|-----------|---------|-------|
| `.pdf` | Docling | best quality, preserves tables |
| `.docx`, `.odt` | Docling | |
| `.pptx` | Docling | slides → sections |
| `.xlsx`, `.ods` | Docling | tables → Markdown |
| `.html`, `.htm` | Docling | |
| `.md`, `.txt`, `.rst` | direct read | no conversion needed |
| `.jpg`, `.jpeg`, `.png`, `.webp`, `.gif` | nomic vision | 768d image embedding, no text chunks |

## Script: `ingest.py`

Lives at `/opt/stacks/ai/ingest/ingest.py`. Runs directly on CT 104.

### Usage

```bash
# Index everything under a path
python3 /opt/stacks/ai/ingest/ingest.py --path /mnt/pve/unas/Notizen

# Different collection, force re-index
python3 /opt/stacks/ai/ingest/ingest.py \
  --path /mnt/pve/unas/Dokumente \
  --collection work-docs \
  --force

# Dry run (print files, don't embed)
python3 /opt/stacks/ai/ingest/ingest.py --path /path/to/docs --dry-run
```

### State file

`/opt/stacks/ai/ingest/state/<collection>.json` tracks `{path: {hash, indexed_at}}`.
Subsequent runs skip unchanged files. Delete the state file to force full re-index.

### Qdrant collection schema

```python
vectors_config = VectorParams(size=768, distance=Distance.COSINE)
# payload per point:
{
    "path":      "/mnt/pve/unas/Notizen/someFile.md",
    "title":     "someFile",        # filename without ext
    "chunk_idx": 0,                 # 0-based chunk index within file
    "total_chunks": 3,
    "text":      "…chunk content…", # empty string for images
    "type":      "markdown",        # markdown | pdf | docx | image | …
    "mtime":     1716700000.0,
    "hash":      "a3f…",            # sha256 of file content
    "source":    "filesystem",
}
```

### Chunking strategy

For Markdown output from Docling (and raw .md/.txt):
1. Split on `## ` / `### ` headers first (keep header as first line of chunk)
2. If chunk > 1200 chars, split further on double-newline (`\n\n`)
3. If still > 1200 chars, hard-split with 150-char overlap

Images: single point per file, no chunking.

### Error handling

- Docling timeout (>60s): skip file, log to `ingest_errors.log`, continue
- Qdrant upsert failure: retry 3×, then log and continue
- Re-run is safe: state file prevents double-indexing

## Building and running

```bash
# On CT 104
mkdir -p /opt/stacks/ai/ingest
cd /opt/stacks/ai/ingest

# Install deps (lightweight — no torch needed, calls services via HTTP)
pip3 install qdrant-client requests tqdm

# Run
python3 ingest.py --path /mnt/pve/unas/Notizen
```

No container needed for the script itself — it runs on CT 104 bare Python and calls
`http://docling:5001`, `http://nomic:80`, `http://qdrant:6333` via `ai-internal` (all
on the same Docker network/host).

To run it from _outside_ CT 104 (e.g. the PVE host), wrap it in a container later.

## Open WebUI integration

OWUI's knowledge base already points at Qdrant (`VECTOR_DB=qdrant`). To surface
documents from the `documents` collection in chat:

1. Admin → Knowledge → Create Knowledge Base
2. Name: "Local Documents"
3. The collection is populated by `ingest.py` — OWUI will search it on `#`-prefixed
   RAG queries or when the knowledge base is enabled in a chat.

> **Note**: OWUI creates its own internal collection names. To share the same `documents`
> collection between `ingest.py` and OWUI, use the OWUI API to create a knowledge base
> pointing to the pre-populated collection — or let OWUI manage its own collection and
> have `ingest.py` add documents via the OWUI knowledge API (`POST /api/v1/knowledge/{id}/file/add`).
> The OWUI API path is cleaner for OWUI search integration; the direct Qdrant path is
> better for external tools (n8n, MCP).

## Ongoing ingestion (n8n)

After the one-shot crawl, wire n8n for continuous ingestion:

| Trigger | n8n nodes | Notes |
|---------|-----------|-------|
| Nextcloud webhook (file created/modified) | HTTP → SSH → `ingest.py --path <file>` | Nextcloud admin → Webhooks app |
| Paperless post-consume webhook | HTTP → Docling → nomic → Qdrant upsert | Paperless has `POST_CONSUME_SCRIPT` hook |
| Cron re-scan | Schedule → SSH → `ingest.py --path /mnt/pve/unas/Notizen` | weekly full re-sync |

The cron re-scan is safe because `ingest.py` skips unchanged files via state hash.

## Paths available on CT 104

| Path | Contents |
|------|---------|
| `/mnt/pve/unas/Notizen/` | Obsidian vault (Markdown) |
| `/mnt/pve/unas/` | full UNAS NFS share |
| `/opt/stacks/*/` | stack configs (already in Git, lower priority) |

Nextcloud files are on CT 105 (`192.168.1.41`). Access via:
- WebDAV: `https://nc.nuclide.systems/remote.php/dav/files/fkrebs@nucli.de/`
- Or mount the NC data volume — not currently mounted on CT 104

## Status

**Not yet implemented.** Design only. Next step: write `ingest.py`.