What this prompt does
This prompt builds a Python PDF processing and report-generation system for a specific [use_case], scaled to [pdf_volume] PDFs per day at [pdf_size] pages each. It covers the full pipeline: extracting text, tables, images, and metadata; analyzing the extracted content; generating formatted PDF reports; batch processing a watched folder; handling corrupted or encrypted files; merge/split utilities; and a Click CLI. Spelling out the messy extraction edge cases up front is what keeps a PDF pipeline from breaking in production.
The variables target the failure-prone parts. [pdf_libraries] and [text_extraction_method] choose the extraction stack, including an OCR fallback for scanned pages, while [encoding_issues] names the character-set edge cases to handle. [analysis_type] defines what structured data you pull out, [generation_library] and [font_family] shape the output reports, and [concurrency_model], [password_handling], and [max_pages] govern batch throughput and how corrupted or protected files are handled rather than crashing the run.
When to use it
- Turning a stream of incoming PDFs into structured data on a schedule.
- Building invoice or document extraction that must survive scanned and encrypted files.
- Generating professional, formatted PDF reports with a cover page, TOC, and charts.
- Running a watched-folder batch pipeline with archiving and error reports.
- Merging or splitting PDFs by page range, chapter markers, or bookmarks.
- Wrapping the whole thing in a Click CLI with extract, analyze, generate, and batch commands.
Example output
You get a modular Python system with code: an extraction module using [text_extraction_method] that pulls text with page numbers, tables, images, and metadata while handling [encoding_issues]; an analysis pipeline performing [analysis_type] with progress reporting; a generation module using [generation_library] producing reports with a cover page, clickable TOC, styled tables, and embedded charts in [font_family]; a batch pipeline watching [input_directory] with [concurrency_model] parallelism; error handling for corrupted and password-protected files via [password_handling]; merge/split utilities; and a Click CLI exposing each command.
Pro tips
- Always include the OCR fallback in
[text_extraction_method]; scanned PDFs have no embedded text, and assuming they do is the most common pipeline failure. - Name the real
[encoding_issues]you expect (BOM, Latin-1, CJK); generic extraction silently mangles non-ASCII text. - Set
[max_pages]as a guardrail so a single enormous file cannot stall the whole[pdf_volume]batch. - Handle
[password_handling]explicitly: try known passwords, then log and skip, rather than letting one locked file crash the run. - Match
[concurrency_model]to the work; PDF parsing is CPU-bound, so a process pool usually beats threads. - Move originals to an archive after processing so re-runs do not reprocess the same files endlessly.
- Expose each stage through the Click CLI (extract, analyze, generate, batch, merge, split) so you can test one step in isolation before wiring the full pipeline.
