Tutorials¶
Five tutorials, in order. Each one builds on the last and ends where the next begins. All of them run on a laptop; only the last needs a GPU, and it says exactly where.
They share one convention with the rest of this documentation: every step ends in a
✓ look for block stating what a correct result looks like. If your output disagrees with one,
stop there. A mismatch is information — it tells you which step broke, which is far more useful than
discovering at the end that something did.
| Tutorial | You will build | Needs | |
|---|---|---|---|
| 1 | Your first pipeline | A folder of documents turned into an AI-ready corpus + knowledge graph, stage by stage. | Nothing but the workspace. No network, no downloads, no GPU. |
| 2 | Understanding the output | Fluency in every emitted artifact — and the ability to trace a graph edge back to the sentence it came from. | Tutorial 1's output. |
| 3 | Retrieval | A four-signal query path over the emitted files: dense, sparse, BM25 and graph traversal, fused and packed. | Tutorial 1's output + one extra. |
| 4 | Bringing your own Provider | A real Provider package of your own, registered as a plugin and green against the conformance suite. | The workspace. |
| 5 | Production | Served GPU models, staged runs, checkpoint/resume, and incremental corpus deltas. | Tutorials 1–2; a GPU for the served-model sections. |
Before you start¶
Work through the quickstart first if you have not — it installs the workspace
and proves it end to end in one command. These tutorials assume you are inside a clone with
uv sync already run.
They also assume the vocabulary from Concepts & Architecture:
- a Capability is a narrow, model-agnostic interface (Parser, EntityExtractor, Embedder, …);
- a Provider is a concrete implementation of one, shipped as its own package and discovered as a plugin;
- a Stage binds one Provider to one node of a pipeline DAG;
- the Runner executes that DAG; the Storage seam is where every artifact lands.
If you want the reasoning rather than the mechanics — why the pipeline extracts narrow and resolves wide, why retrieval is stateless, what the knowledge graph actually buys — read the deep dive. It is the argument these tutorials are the practice for.