generate-pdf

name	generate-pdf
description	Generate a PDF from ingested source documents using the agnostic Document tree
model	deepseek/deepseek-v4-pro
tier	pro
inputs	[{"name":"source_document_ids","type":"list[str]","required":true,"description":"Prisma Document.id values to ground generation in"},{"name":"brief","type":"str","required":true,"description":"What the user wants generated"},{"name":"style","type":"str","required":false,"description":"Tone/style hint (e.g. \"clinical\", \"casual\", \"technical\")"},{"name":"audience","type":"str","required":false,"description":"Target reader description"},{"name":"target_length","type":"str","required":false,"description":"short | medium | long (default: medium)"}]
outputs	[{"name":"file_path","type":"str"},{"name":"file_url","type":"str"},{"name":"title","type":"str"},{"name":"node_count","type":"int"},{"name":"model","type":"str"},{"name":"source_document_ids","type":"list[str]"}]

generate-pdf

Generates a PDF document from one or more ingested source documents stored in the OfficePlane database.

What it does

1. Loads the requested source documents from Postgres via Prisma (chapters, sections, summaries).
2. Prompts DeepSeek v4-flash to produce an agnostic Document JSON tree aligned to the CommonMark / Pandoc AST vocabulary — NOT the legacy modules → lessons → blocks schema.
3. Parses the JSON into a Document dataclass tree via parse_document.
4. Renders the tree to .docx bytes via render_docx.
5. Converts the .docx to .pdf using libreoffice --headless (already installed in the api Docker image — no extra pip deps required).
6. Writes the result to /data/workspaces/<job_id>/output.pdf.

PDF conversion path

The PDF backend uses libreoffice's headless conversion mode:

Document tree → render_docx() → doc.docx → libreoffice --convert-to pdf → doc.pdf

This reuses the libreoffice binary already present in the api image for DOCX→PDF ingestion. No new pip dependencies are introduced.

Expected JSON shape

The LLM must return a strict JSON object conforming to the agnostic Document schema:

{
  "type": "document",
  "schema_version": "1.0",
  "meta": {"title": "My Document", "language": "en"},
  "children": [
    {
      "type": "section",
      "id": "s1",
      "level": 1,
      "heading": "Introduction",
      "children": [
        {"type": "paragraph", "text": "Opening prose here."},
        {
          "type": "list",
          "ordered": false,
          "items": [
            {"type": "paragraph", "text": "First bullet"},
            {"type": "paragraph", "text": "Second bullet"}
          ]
        }
      ]
    }
  ],
  "attributions": [
    {
      "node_id": "s1",
      "document_id": "<source-doc-uuid>",
      "section_id": "<source-section-id>"
    }
  ]
}

Key rules:

• Sections nest recursively. Use level 1..6 to indicate depth.
• Block types: heading, paragraph, list, table, figure, code, callout, quote, divider.
• Never emit "modules" or "lessons" — use nested sections instead.
• Every non-trivial node should have an id (short string).
• Provide at least one attribution per major section pointing back to a source document_id.

Minimal output example

{
  "type": "document",
  "meta": {"title": "BP Primer"},
  "children": [
    {
      "type": "section",
      "id": "intro",
      "level": 1,
      "heading": "Why Measure Blood Pressure",
      "children": [
        {"type": "paragraph", "text": "Early detection of hypertension saves lives."}
      ]
    }
  ],
  "attributions": [
    {"node_id": "intro", "document_id": "doc-abc123"}
  ]
}