generated from atu/saiop-infrastructure
Claude Code
49 lines
2.1 KiB
Markdown
49 lines
2.1 KiB
Markdown
---
|
|
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.
|