Pipeline hub

Choose the right inference path, then dive into guides and notebooks

Pipelines

smol-doc-analyzer ships two complementary inference chains. Use this page as the map; each card links to design notes, CLI usage, and rendered notebooks.

flowchart TB
  Q{Need paper Fig. 1<br/>medical / salvage<br/>image-first flow?}
  Q -->|Yes| DICIE[DICIE · src/docie/]
  Q -->|No — need memos / ACORD intake| MEMO[Memo chain · src/pipeline/]
  DICIE --> DN[Notebook: DICIE walkthrough]
  MEMO --> MN[Notebook: Pipeline walkthrough]

When to pick DICIE vs the memo chain

DICIE — Document Image Classification & Information Extraction

DICIE

Design notes

Stages, taxonomies, review gates, FastAPI serve shape.

Notebook

DICIE walkthrough

Stage-by-stage demo with page images and stored outputs.

CLI

Usage · DICIE

python -m src.docie batch + response-only recipes.

flowchart LR
  A[Document Processing] --> B[Classification]
  B --> C[Information Extraction]
  C --> D[Aggregate / respond]

DICIE stage order (paper Fig. 1)

Memo chain — markdown → classify → extract → vision → summarize

Memo

Architecture

Chronological reaction contract and markdown conversion.

Notebook

Pipeline walkthrough

End-to-end memo-chain analysis with figures.

CLI

Usage · orchestrator

python -m src.pipeline.orchestrator and batch_runner.

flowchart LR
  A[to_markdown] --> B[classify]
  B --> C[extract]
  C --> D[vision_llm]
  D --> E[summarize]

Memo-chain chronological stages

Supporting systems

System Role Links
Sample corpus Queryable synthetic medical / salvage store Guide · Walkthrough
Classical RF TF-IDF + Random Forest baseline Notebook
Discord (Chloride) /analyze memo-chain front-end Usage
Provenance Synthetic-only disclosure Data Provenance

See also

Quick Start · Commands · Implementation plan

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