rakam-systems-documents

Generic document preparation: turn any source document into clean, structured, provenance-tagged content the rest of the stack can reason over.

from rakam_systems_documents import prepare, MistralOCRProvider

pc = prepare(raw_bytes, mime="application/pdf", filename="invoice.pdf",
             ocr=MistralOCRProvider())      # ocr optional; native PDFs skip it
pc.markdown      # what a model reads
pc.rows          # structured rows (tabular sources), header-keyed + provenance
pc.provenance    # page/sheet/row refs
pc.provider      # pdf_text | mistral_ocr | docling_ocr | tabular | text | none

What it is (and isn't)

One job: bytes -> PreparedContent. Format dispatch, parse/decode, hybrid-OCR routing, provenance normalization, truncation. A pure function — the only I/O is the injected OCR provider.

Not its job: storage, chunking, embedding, indexing, async/status, or any business meaning (extraction, matching, classification). Those belong to the caller — the library gives every caller one document→content primitive so none reimplements parsing/OCR.

Formats

OCR is pluggable

Native-text PDFs never hit OCR. Scanned pages / images route to an OCRProvider:

• MistralOCRProvider (default) — the Mistral hosted OCR API (/v1/ocr). Needs MISTRAL_API_KEY.
• DoclingOCRProvider — on-prem, no egress. Requires the optional extra: pip install rakam-systems-documents[docling].

A missing/unavailable engine degrades gracefully (native text still works; a scan yields provider="none" + a help hint, never an exception).

Install

pip install rakam-systems-documents            # Mistral OCR path
pip install rakam-systems-documents[docling]    # + on-prem OCR