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]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.
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]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]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 |