autogame-17/pdf2gep

pdf2gep

Convert a PDF into GEP bundles that can be uploaded to EvoMap as retrieval / reference knowledge.

Scope (read first)

pdf2gep is a protocol adapter for reference knowledge, not procedural knowledge.

• The emitted Capsule has source_type = "pdf_knowledge", outcome.status = "knowledge_reference", and an empty execution_trace. It is NOT evidence that the associated Gene has been validated in practice.
• For procedural knowledge (a SKILL.md describing a workflow plus real executions), use skill2gep instead.
• The GEP paper (Wang, Ren, Zhang, arXiv:2604.15097) validates Gene-as-control-interface on 45 code-science tasks. That result does not carry over to retrieval Genes; treat pdf2gep output as retrieval material.

Usage

node index.js <pdf_url_or_path>

Output: temp/evomap_assets/batch_<timestamp>.json, an array of { gene, capsule } entries.

Workflow

1. Fetch/Read -- Download the PDF from URL (browser User-Agent) or read the local file. Record the PDF's sha256.
2. Extract -- Use pdf-parse-fork to pull out raw text.
3. Chunk -- Fixed-width split of ~4000 chars per chunk (not semantic; see README scope note). Record each chunk's sha256.
4. Wrap -- Build a knowledge_reference Gene + pdf_knowledge Capsule per chunk (GEP schema 1.6.0).
5. Save -- Write the batch to temp/evomap_assets/batch_<ts>.json.

Publishing

Use evolver (the GEP reference runtime, https://github.com/EvoMap/evolver) to publish:

evolver publish --bundle temp/evomap_assets/batch_<ts>.json

The hub routes pdf_knowledge Capsules into the retrieval index, separate from execution Capsules.

Dependencies

• pdf-parse-fork
• Node.js 18+ (built-in fetch)

Invariants for consumers

• outcome.status === "knowledge_reference"
• execution_trace is always empty
• blast_radius.files === 0 && blast_radius.lines === 0
• source_type === "pdf_knowledge" on both the Gene _source and the Capsule

Validators that expect execution Capsules MUST filter these out (or treat them explicitly as reference material).