petey 0.1.4

Petey — The Easy PDF Extractor

Petey

Meet Petey. Petey gets your data out of PDFs and into formats that you can actually work with. It formats weird tables into easy-to-analyze CSV documents. It can even answer questions about what's in your data. It's free to use, beyond a negligible hit to your OpenAI or Anthropic account.

pip install petey

Why Petey?

The PDF format was designed to look identical on any screen or printer. It was format and technology agnostic, a universal container for the printed page. But all that mattered was its visual presentation. As long as it rendered correctly, the internal representation didn't matter.

And so the inside of a PDF is often chaotic. It is just a bunch of items — words, characters, shapes, images — and their coordinates, with little or no regard for the relationship between anything. What reads as one cohesive line of text could be three groups of words that happened to be positioned sequentially with the same y-value. You have probably experienced this if you have ever tried to select text in a PDF and find yourself highlighting disconnected words on four different lines.

The primary challenge of getting your data out of a PDF is imposing some order on this code soup. A lot of hard-working folks have developed methods and tools to accomplish this over the years, finding ingenious ways to infer meaning from the relative positions of all the elements of a document. But it's tricky, precise work.

As it turns out, AI can be a big help here. It turns out you don't need a particularly advanced LLM to interpret some fairly difficult PDFs. But models need infrastructure. Not everyone has time to teach themselves how to be an amateur ML engineer, even with the magic of vibe coding.

Petey wires everything together for you. Just pass it your files and a schema that explains what you want, and it will return a JSON or CSV with your data.

Architecture

Text Extractors

The first step is getting text out of the PDF. Different extractors work better for different document types.

Parser	Install	Best for	How it works
pymupdf	included	Most documents	Reads the embedded text layer directly. Fast and reliable. Default for single-record schemas.
tables	included	Bordered tables	Uses PyMuPDF's table detection to find cells and extract them as TSV. Falls back to plain text for pages without tables. Default for array schemas.
pdfplumber	included	Borderless tables	Layout-preserving extraction that positions text spatially. Good for documents where columns are aligned by whitespace rather than cell borders.
tabula	pip install petey[tabula]	Structured tables	Uses tabula-py (Java-based) to detect and extract tables as DataFrames. Falls back to pymupdf for pages without tables. Requires Java.

OCR Backends

If a PDF has no embedded text layer (e.g. a scanned document), Petey can fall back to OCR. OCR is only triggered when the extracted text is very short (< 100 characters), so it won't slow down documents that already have text.

Backend	Install	How it works
none	—	No OCR. Default.
tesseract	included	Local OCR via pytesseract. Requires the Tesseract binary installed on your system.
mistral	pip install petey[mistral-ocr]	Cloud OCR via Mistral's mistral-ocr-latest model. Sends page images to the Mistral API. Requires MISTRAL_API_KEY.

LLM Backends

The LLM interprets the extracted text and returns structured data matching your schema. Petey auto-detects the right backend from the model name, or you can set it explicitly.

Backend	Install	Models	Auto-detected when
openai	included	gpt-4.1-mini, gpt-4o, etc.	Model name doesn't match other patterns (default)
anthropic	included	claude-sonnet-4-6, claude-haiku-4-5-20251001, etc.	Model starts with claude
litellm	included	Gemini, Mistral, Ollama, Bedrock, Vertex AI, Cohere, 100+ more	Model starts with gemini/, mistral/, ollama/, bedrock/, etc.

Setup

Add your API key to a .env file in your working directory:

OPENAI_API_KEY=sk-...

Or for Anthropic:

ANTHROPIC_API_KEY=sk-ant-...

Or for any provider via litellm (e.g. Gemini):

GEMINI_API_KEY=...

Petey defaults to gpt-4.1-mini, which is fast and cheap and handles most documents well. To use a different model, pass --model on the CLI or set a default in .env:

PETEY_MODEL=claude-sonnet-4-5

Schemas

Every extraction starts with a schema — a simple YAML file that tells Petey what fields to look for and what type of data to expect in each one.

name: Invoice
fields:
  vendor:
    type: string
    description: Company name on the invoice
  amount:
    type: number
    description: Total amount due
  date:
    type: date
  status:
    type: category
    values: [Paid, Unpaid, Overdue]

Field types

Type	Notes
string	Any text value
number	Integer or decimal
date	Returns ISO 8601 format
category	Constrained set of values; optionally list values: to enforce them

All fields are nullable — Petey returns null for anything it can't find rather than guessing.

Schema options

Option	Description
record_type: array	Extract multiple records per document instead of one
instructions	Extra guidance appended to the prompt (e.g. "ignore the summary row")
header_pages	Number of leading pages to treat as a document header (see below)
pages	Page range to process, e.g. "2-5" or "1,3,5-7" (1-indexed)
parser	Text extraction backend: pymupdf (default), tables, pdfplumber, or tabula

Use Cases

One record per file

The simplest case: you have a pile of documents that all contain the same kind of information, and you want one row of data out of each one. Think invoices, contracts, permit applications, or any form where each file is a self-contained record. This works on narrative text too — if you have 200 complaint letters and want to extract the filer name, date, and subject from each one, Petey handles that just as well as a structured form.

Petey processes each file and returns a single structured record. Run it against a folder and you get a CSV with one row per file.

name: Invoice
fields:
  vendor:
    type: string
    description: Name of the vendor
  invoice_number:
    type: string
  date:
    type: date
  amount_due:
    type: number
  status:
    type: category
    values: [Paid, Unpaid, Overdue]

petey extract --schema invoice.yaml ./invoices/ -o results.csv

One table per file

Some documents contain a table of records — a bank statement, a schedule of assets, a list of transactions. Add record_type: array and Petey will return multiple rows per file. Petey splits the document into pages, processes each concurrently, and assembles the results in document order.

Documents like this often have important context on the first page — a header, column labels, the filer's name. The header_pages option tells Petey to prepend those pages to every page it sends to the LLM, so that context is always visible no matter how deep into the document it's looking.

name: Transactions
record_type: array
header_pages: 1
fields:
  date:
    type: date
  description:
    type: string
  amount:
    type: number

petey extract --schema transactions.yaml statement.pdf -o results.csv

You can point this at a folder of files and Petey will combine all the rows into a single output, with a _source_file column on each row. Use pages to target a specific range if the table only occupies part of the document (e.g. pages: "3-22").

Scanned documents

If your PDFs are scanned images with no text layer, use an OCR backend:

# Local OCR with Tesseract
petey extract --schema schema.yaml --ocr tesseract ./scans/ -o results.csv

# Cloud OCR with Mistral (better quality, requires API key)
petey extract --schema schema.yaml --ocr mistral ./scans/ -o results.csv

Using different LLM providers

# OpenAI (default)
petey extract --schema schema.yaml --model gpt-4.1-mini ./pdfs/

# Anthropic
petey extract --schema schema.yaml --model claude-sonnet-4-6 ./pdfs/

# Gemini via litellm
petey extract --schema schema.yaml --model gemini/gemini-2.0-flash ./pdfs/

# Ollama (local)
petey extract --schema schema.yaml --model ollama/llama3 ./pdfs/

# Explicit backend override
petey extract --schema schema.yaml --model my-model --llm-backend litellm ./pdfs/

CLI Reference

petey extract --schema schema.yaml ./pdfs/ -o results.csv

Flag	Default	Description
--schema / -s	required	Path to your YAML schema
--model / -m	gpt-4.1-mini	Model ID to use
--concurrency / -c	10	Number of concurrent API calls
--output / -o	stdout	Output file path
--format / -f	inferred	csv, json, or jsonl
--instructions / -i	—	Extra extraction instructions
--parser	pymupdf	Text extraction backend (pymupdf, tables, pdfplumber, tabula)
--ocr	none	OCR backend (none, tesseract, mistral)
--llm-backend / -b	auto-detect	LLM backend (openai, anthropic, litellm)
--pages-per-chunk / -p	1 for arrays	Pages per LLM call (set to 0 to disable chunking)

Python API

from petey import extract, load_schema

# load_schema returns a Pydantic model class and the raw spec dict
schema, spec = load_schema("invoice.yaml")

# Basic extraction
result = extract("invoice.pdf", schema)

# With options
result = extract(
    "invoice.pdf",
    schema,
    model="claude-sonnet-4-6",      # LLM model ID
    parser="pdfplumber",            # text extraction backend
    ocr_backend="mistral",          # OCR for scanned docs
    llm_backend="litellm",          # explicit LLM backend
    instructions="Ignore the watermark text",
)

Optional Dependencies

pip install petey                    # Core (includes tesseract OCR + litellm)
pip install petey[mistral-ocr]       # + Mistral OCR
pip install petey[tabula]            # + Tabula table extraction (requires Java)