058362b87f
Initial skeleton for the deterministic compound-AI pipeline that turns `tolkien deploy <name>` into a valinor scaffold. v0 wires one classifier (`db_kind`) end-to-end against the Ollama-on-radagast backend so we can iterate prompts with confidence before adding the rest. - pyproject.toml: uv project, Python 3.13, httpx/pydantic/typer/pyyaml. - src/tolkien/llm.py: single-turn /api/chat wrapper. JSON-mode + temp 0 + 30m keep-alive. Pydantic schema validation on parse. - src/tolkien/cli.py: `tolkien classify db-kind --doc <file>` runs the classifier and prints JSON. `tolkien deploy <name>` is stubbed. - src/tolkien/prompts/db_kind.md: tight system prompt with 1-shot example and explicit "return ONLY JSON" guard. - src/tolkien/schemas.py: DbKindResult pydantic model. - eval/cases/mealie.yml + run_eval.py: regression harness. Currently one case (mealie); failures print field-level diffs. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
1.6 KiB
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 .