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Getting Started

Latence produces two AI-ready deliverables from a folder of messy documents — a RAG-ready corpus and a knowledge graph, both as portable files. This section gets you from an empty environment to those outputs.

The one-command path

Everything ships CPU-first and offline by default (ADR-0007), so you can run a full pipeline on a laptop with no GPU and no network:

# Install from a clone — nothing is published to PyPI (see the note below).
git clone https://github.com/ddickmann/latence
cd latence
uv sync

# 1. A guided wizard emits ONE opinionated, quality-baked stack config (ADR-0041).
uv run latence setup

# 2. Run that config end-to-end over your documents.
uv run latence process --config stack.yaml --input ./my-documents --out ./corpus

Why a clone, and not pip install

Nothing is published to PyPI: the distribution channel is the signed GitHub Release (ADR-0062, whose rationale is superseded and whose decision is reopened — not reversed — by ADR-0063). A pip install latence-core today fails with ERROR: No matching distribution found. uv sync installs the whole CPU-first workspace from the clone, which is what every command on this page assumes; the quickstart walks the same install step by step.

latence process runs the wizard-produced stack and writes the RAG corpus + knowledge graph, plus a first-class Quality Report substantiating the "AI-ready" claim.

# 3. Read the results — a read-only, offline, localhost console over what the run wrote.
#    `latence-console` is already in the environment `uv sync` built.
uv run latence-console ./corpus

latence-console serves the run list and each run's Quality Report as headline quality verdicts a non-engineer can read, not raw JSON. It writes nothing, holds no state, and makes no network requests — see Read the results.

Run it stage by stage

To understand what each Stage does — and to swap in a learned or GPU Provider and see the quality lift — follow the walkthrough:

  • Fresh-pod walkthrough From a bare GPU pod to AI-ready data over a mixed-file corpus with the enterprise-SOTA pipeline (served-vLLM OCR, GLiNER2 extraction, the GLinker entity-linker, Granite r2 embeddings) — every Stage in isolation, then the full end-to-end run.

  • GPU testing guide Run the whole learned system yourself: the GPU Providers, validated on a CUDA pod.

The core CLI

The latence CLI (installed with latence-core) is the entry point for every workflow:

Command What it does
latence setup Guided wizard → one opinionated, quality-baked stack config (ADR-0041).
latence process Run a full stack config end-to-end over a document folder.
latence run Run an explicit Pipeline file.
latence stack validate Validate a stack config end-to-end (see Run & validate a stack).
latence bake-off Compare Providers on a Capability (see Run a bake-off).
latence tune Auto-tune a stack per device (see Tune a stack per device).
latence delta Run an incremental corpus delta (ADR-0018).
latence retract / latence purge Soft-tombstone or hard-erase documents (GDPR).

The read-only run console ships as its own package (pip install latence-console) so the core CLI stays dependency-thin:

Command What it does
latence-console <location> Serve the read-only run console over a Storage location (Read the results).

Where to go next