tolkien

Local-LLM orchestrator that turns tolkien deploy mealie into a complete valinor GitOps scaffold (config.yaml, values.yaml, CNPG cluster, Vault secrets, Terraform stubs) ready for the operator to review and PR.

Architecture

Compound-AI pipeline, not a ReAct loop. Each step in the deploy workflow is a single LLM call that produces structured JSON — the surrounding logic is plain Python. This shape works reliably on a CPU-served 7B model where a free-form agent loop would drift.

deploy <name>
  ├── search.find_helm_chart()
  ├── classify.has_chart(results)        — LLM, JSON
  ├── fetch.app_docs(url)
  ├── classify.db_kind(docs)             — LLM, JSON
  ├── classify.needs_object_storage(docs)— LLM, JSON
  ├── classify.secrets_vs_config(env)    — LLM, JSON
  ├── classify.storage_class(docs)       — LLM, JSON
  └── render.scaffold(facts)             — Jinja2 → ./out/<name>/

Backend

Ollama running on radagast (http://radagast.jpnadas.xyz:11434), serving Qwen2.5-Coder-7B-Instruct Q4_K_M. See valinor/ansible/playbooks/radagast-setup.yml for the host setup.

v0 scope

• Single classifier (db_kind) wired end-to-end against the live model
• CLI: tolkien classify db-kind --doc <file> prints JSON
• Eval harness with one case (mealie)
• Output to stdout / ./out/; no git push, no FastAPI, no Redis

v1 layers on FastAPI + Redis + a Gitea bot to push branches; v2 wires a Gitea webhook for issue-comment triggers.

Develop

uv sync
uv run tolkien --help
uv run pytest
uv run ruff check .