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>
50 lines
1.6 KiB
Markdown
50 lines
1.6 KiB
Markdown
# tolkien
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Local-LLM orchestrator that turns `tolkien deploy mealie` into a complete valinor
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GitOps scaffold (config.yaml, values.yaml, CNPG cluster, Vault secrets, Terraform
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stubs) ready for the operator to review and PR.
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## Architecture
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Compound-AI pipeline, not a ReAct loop. Each step in the deploy workflow is a
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single LLM call that produces structured JSON — the surrounding logic is plain
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Python. This shape works reliably on a CPU-served 7B model where a free-form
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agent loop would drift.
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```
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deploy <name>
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├── search.find_helm_chart()
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├── classify.has_chart(results) — LLM, JSON
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├── fetch.app_docs(url)
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├── classify.db_kind(docs) — LLM, JSON
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├── classify.needs_object_storage(docs)— LLM, JSON
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├── classify.secrets_vs_config(env) — LLM, JSON
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├── classify.storage_class(docs) — LLM, JSON
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└── render.scaffold(facts) — Jinja2 → ./out/<name>/
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```
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## Backend
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Ollama running on radagast (`http://radagast.jpnadas.xyz:11434`), serving
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Qwen2.5-Coder-7B-Instruct Q4_K_M. See valinor/`ansible/playbooks/radagast-setup.yml`
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for the host setup.
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## v0 scope
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- Single classifier (`db_kind`) wired end-to-end against the live model
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- CLI: `tolkien classify db-kind --doc <file>` prints JSON
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- Eval harness with one case (mealie)
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- Output to stdout / `./out/`; no git push, no FastAPI, no Redis
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v1 layers on FastAPI + Redis + a Gitea bot to push branches; v2 wires a Gitea
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webhook for issue-comment triggers.
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## Develop
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```bash
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uv sync
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uv run tolkien --help
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uv run pytest
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uv run ruff check .
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```
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