---
name: saiop-rag-ingestion
description: "Run the SAIOP document ingestion pipeline (scripts/ingestion/ingest.py) to load docs into Qdrant — network gotchas and how to verify it actually worked."
version: 1.0.0
author: Claude Code (SAIOP ops session)
license: MIT
platforms: [linux]
prerequisites:
  env_vars: [QDRANT_API_KEY]
  commands: [docker]
metadata:
  hermes:
    tags: [SAIOP, RAG, Qdrant, ingestion, Ollama]
---

# RAG ingestion into Qdrant (SAIOP)

## When to use

Loading new SOPs, runbooks, or infrastructure docs into Qdrant so they're
retrievable via RAG (Deployment Guide §6.2).

## Key facts

- The reference bundle's `scripts/ingestion/ingest.py` has a real bug: it
  calls `sys.exit(1)` but never `import sys`. Add `import sys` before
  running it, or it crashes if invoked with neither `--file` nor
  `--directory`.
- It embeds via **Ollama directly** (`OLLAMA_URL/api/embeddings`,
  `nomic-embed-text` model) — NOT through the AI Router. Point `OLLAMA_URL`
  at `http://host.docker.internal:11434` if running the script in a
  container (Ollama is a host systemd service, not a container).
- **UFW only allows port 11434 from specific docker subnets**
  (`172.17.0.0/16`, `172.19.0.0/16` — the `ai-internal` network). A
  container on a *different* network (e.g. `knowledge`, where Qdrant lives)
  will silently time out reaching Ollama via `host.docker.internal`, even
  though the hostname resolves fine. Fix: attach the ingestion container to
  **both** `ai-internal` (for Ollama) and `knowledge` (for Qdrant) —
  `docker run -d --network ai-internal ...` then
  `docker network connect knowledge <container>`.
- The five collections are `atu_infrastructure`, `atu_operations`,
  `atu_incidents`, `atu_governance`, `atu_ai` — pick by content type (the
  script's `doc_type_map` already does chunk-size tuning per collection).
- **Don't trust "uploaded N chunks" alone as proof it worked.** Verify with
  a real semantic search: embed a realistic question via the same Ollama
  endpoint, POST it to `/collections/<name>/points/search`, and check the
  returned `chunk_text` is actually relevant (score > ~0.7 and on-topic) —
  not just that points exist.