vera-ingest

vera-ingest contains VERA's provider-neutral ingestion core: shared types, descriptors, option parsing, a strict ingest-pipeline registry, conversion, chunking helpers (build_chunks_from_blocks for structured layout, chunk_pages / detect_heading for custom page-text pipelines), and archive viewer conventions. Parsers emit ParsedBlock; convert with IngestBlock.from_parsed before returning IngestResult.blocks.

PDF conversion pipelines register through the vera.ingest_pipelines entry-point group. The default pymupdf provider ships as vera-ingest-pymupdf; Docling ships as the optional vera-ingest-docling package. Pipelines return a normalized IngestResult; convert() writes validated archives through one shared atomic path.

Each pipeline owns typed chunking/OCR defaults, validation, and a descriptor of supported fields. New convert() callers should pass parser, pipeline_options, and embedder settings (model / embedding_function / embedder_options). Shared convert accepts opaque pipeline_options on a thin IngestRequest. Legacy kwargs (chunk_size, overlap, ocr_mode, ocr_language, ocr_dpi) remain compatibility aliases; descriptor fields and OCR engine control which aliases are forwarded (Tesseract-shaped ocr_language/ocr_dpi/ocr_download only go to Tesseract pipelines), and explicit pipeline_options win. Omitted convert() aliases mean the pipeline's own default (they are not replaced by 500/eng/…).

It emits ready-made vera_doc.ChunkRecord values and optional opaque attachments, then stores them through vera_doc.VeraDocument. It also provides vera_ingest.viewer helpers that interpret ingest-produced page, figure, region, and source-document conventions.

Install

python -m pip install "vera-ingest>=0.3.0"

For PDF conversion, also install a pipeline plugin (the CLI and desktop app pull in vera-ingest-pymupdf by default):

python -m pip install "vera-ingest-pymupdf>=0.3.0"

From a repository checkout:

python -m pip install ./packages/vera-doc ./packages/vera-ingest ./packages/vera-ingest-pymupdf

See the vera-ingest documentation for concepts, examples, and API reference.

See the conversion guide.

Embedding model selection

convert() and batch_convert() accept either:

• model="hashing" / model="sentence-transformers:all-MiniLM-L6-v2" — resolved through vera_doc.get_embedder before PDF parsing begins, or
• embedding_function=<object> — any object with model_name, dimension, and embed(texts).

Unknown model specs raise vera_doc.UnknownEmbeddingModelError. To add a named provider without forking VERA, register a factory with vera_doc.register_embedder or ship a vera.embedders entry-point plugin.