billfox 0.2.11

Composable document data extraction: load, preprocess, OCR, LLM parse, store with vector search.

billfox

AI-agent-friendly receipt management -- extract, parse, store, and search receipts and invoices with composable pipelines built for LLM workflows.

billfox is a Python library for building document processing pipelines from independent, swappable stages. Load a document, preprocess it, OCR the text, parse it with any LLM into structured data, and store it with hybrid vector search -- each stage implements a simple protocol, so you can mix built-in modules with your own.

How It Works

graph LR
    A["Source<br/><small>LocalFileSource</small>"] --> B["Preprocessor<br/><small>Resize, YOLO, Chain</small>"]
    B --> C["Extractor<br/><small>Mistral OCR, Docling</small>"]
    C --> D["Parser<br/><small>LLMParser (any LLM)</small>"]
    D --> E["Store<br/><small>SQLite + hybrid search</small>"]

    style A fill:#4a90d9,stroke:#357abd,color:#fff
    style B fill:#6c757d,stroke:#565e64,color:#fff
    style C fill:#e67e22,stroke:#cf6d17,color:#fff
    style D fill:#27ae60,stroke:#1e8449,color:#fff
    style E fill:#8e44ad,stroke:#763895,color:#fff

Every boundary is a protocol -- implement DocumentSource, Preprocessor, Extractor, Parser[T], Embedder, or DocumentStore[T] to plug in your own components.

Stage	Protocol	Built-in
Source	DocumentSource	LocalFileSource
Preprocessor	Preprocessor	ResizePreprocessor, YOLOPreprocessor, PreprocessorChain
Extractor	Extractor	MistralExtractor, DoclingExtractor
Parser	Parser[T]	LLMParser[T]
Embedder	Embedder	OpenAIEmbedder
Store	DocumentStore[T]	SQLiteDocumentStore[T]

Installation

pip install billfox                    # Core only (types and protocols)
pip install 'billfox[mistral]'         # + Mistral OCR
pip install 'billfox[llm]'             # + LLM parsing (pydantic-ai)
pip install 'billfox[store]'           # + SQLite storage and search
pip install 'billfox[all]'             # Everything

All available extras

Extra	Packages	Use case
mistral	mistralai	Mistral OCR extraction
yolo	onnxruntime, numpy, Pillow	YOLO document cropping
llm	pydantic-ai	LLM structured parsing
openai	openai	OpenAI text embeddings
anthropic	anthropic	Anthropic LLM support
store	sqlalchemy, aiosqlite, sqlite-vec	SQLite storage + search
google-drive	google-api-python-client, google-auth	Google Drive backup
cli	typer, rich, tomli-w	Command-line interface
all	All of the above	Everything

Quick Start

Extract Markdown from a Document (OCR only)

import asyncio
from billfox.source import LocalFileSource
from billfox.extract import MistralExtractor

async def main():
    source = LocalFileSource()
    extractor = MistralExtractor()  # uses MISTRAL_API_KEY env var

    doc = await source.load("invoice.pdf")
    result = await extractor.extract(doc)
    print(result.markdown)

asyncio.run(main())

Full Pipeline -- OCR + LLM Parse + Store

import asyncio
from pydantic import BaseModel
from billfox import Pipeline
from billfox.source import LocalFileSource
from billfox.extract import MistralExtractor
from billfox.parse import LLMParser
from billfox.preprocess import ResizePreprocessor
from billfox.store import SQLiteDocumentStore

class Invoice(BaseModel):
    vendor_name: str
    total: float
    date: str

async def main():
    pipeline = Pipeline(
        source=LocalFileSource(),
        extractor=MistralExtractor(),
        parser=LLMParser(
            model="openai:gpt-4.1",
            output_type=Invoice,
            system_prompt="Extract invoice fields from this document.",
        ),
        preprocessors=[ResizePreprocessor(max_side=1024)],
        store=SQLiteDocumentStore(db_path="invoices.db", schema=Invoice),
    )

    invoice = await pipeline.run("scan.jpg", document_id="inv-001")
    print(f"{invoice.vendor_name}: ${invoice.total}")

asyncio.run(main())

CLI

# Configure API keys
billfox config set api_keys.mistral sk-...

# Extract markdown via OCR
billfox extract receipt.jpg

# Parse into structured JSON
billfox parse receipt.jpg --schema ./models.py:Receipt --model openai:gpt-4.1

# Search stored documents
billfox search "coffee" --db invoices.db

Extending billfox

Every stage is a Python protocol. Implement the method, pass it to Pipeline, done.

Custom Extractor

from billfox._types import Document, ExtractionResult, Page
from billfox.extract import Extractor

class MyExtractor:
    async def extract(self, document: Document) -> ExtractionResult:
        text = await call_my_ocr_service(document.content)
        return ExtractionResult(
            markdown=text,
            pages=[Page(index=0, markdown=text)],
            metadata={},
        )

Custom Preprocessor

from billfox._types import Document
from billfox.preprocess import Preprocessor

class GrayscalePreprocessor:
    async def process(self, document: Document) -> Document:
        if not document.mime_type.startswith("image/"):
            return document  # pass through non-images
        gray_bytes = convert_to_grayscale(document.content)
        return Document(
            content=gray_bytes,
            mime_type=document.mime_type,
            source_uri=document.source_uri,
            metadata={**document.metadata, "preprocessor": "grayscale"},
        )

Custom Store

from billfox.store import DocumentStore

class MyStore:
    async def save(self, document_id: str, data: T) -> None: ...
    async def get(self, document_id: str) -> T | None: ...
    async def search(self, query: str, *, limit: int = 20) -> list[SearchResult]: ...
    async def delete(self, document_id: str) -> None: ...

See the full documentation for more examples:

• Getting Started
• Custom Extractor
• Custom Preprocessor
• Storage and Search

Core Types

All core types are frozen dataclasses (immutable after creation):

Document(content=b"...", mime_type="image/jpeg", source_uri="receipt.jpg", metadata={})
ExtractionResult(markdown="...", pages=[Page(index=0, markdown="...")], metadata={})
SearchResult(document_id="inv-001", data={...}, score=0.95, signals={...})

Development

Prerequisites

• Python 3.11+
• Git

Setup

git clone https://github.com/billfox-ai/billfox.git
cd billfox
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

The dev extra installs all optional dependencies plus pytest, mypy, ruff, and coverage.

Commands

make test        # Run tests
make lint        # Lint with ruff
make format      # Auto-format with ruff
make typecheck   # Type check with mypy (strict)

Project Structure

src/billfox/
  __init__.py          # Re-exports: Pipeline, Document, ExtractionResult, SearchResult
  _types.py            # Core frozen dataclasses
  _version.py          # Version string
  pipeline.py          # Pipeline compositor
  source/              # Document loading (LocalFileSource)
  preprocess/          # Image preprocessing (resize, YOLO, chain)
  extract/             # OCR / text extraction (Mistral, Docling)
  parse/               # LLM structured parsing
  embed/               # Text embeddings (OpenAI)
  store/               # SQLite storage + hybrid search (BM25 + vector + RRF)
  backup/              # Document backup (local, Google Drive)
  models/              # Pre-built Pydantic models (Receipt)
  cli/                 # Typer CLI application
tests/                 # pytest suite (26 test files)
docs/                  # mkdocs-material documentation

Code Style

• Formatter/linter: ruff (120 char line length)
• Type checker: mypy in strict mode
• Type annotations on all public functions
• Google-style docstrings on public classes/functions
• from __future__ import annotations in all source files (except CLI modules -- typer requires runtime annotations)
• Protocols live in _base.py files with @runtime_checkable
• Lazy imports for optional dependencies with clear ImportError messages

Adding a New Module

1. Create a _base.py protocol if introducing a new stage
2. Implement the protocol in a new file
3. Re-export in the subpackage __init__.py
4. Add optional dependencies to pyproject.toml under a new extra
5. Write tests with mocked external dependencies
6. Add a documentation page under docs/

Contributing

See CONTRIBUTING.md for the full guide. The short version:

1. Fork and create a feature branch from main
2. Implement with tests
3. Run make lint && make typecheck && make test
4. Submit a PR

License

MIT -- see LICENSE for details.