Metadata-Version: 2.5
Name: ai-document-intelligence
Version: 0.3.0
Summary: Python SDK for document intelligence, PDF parsing, and structured extraction for LLM and RAG applications.
Project-URL: Homepage, https://github.com/vishalChoudhary-git/ai-document-intelligence
Project-URL: Repository, https://github.com/vishalChoudhary-git/ai-document-intelligence
Project-URL: Issues, https://github.com/vishalChoudhary-git/ai-document-intelligence/issues
Author: Vishal Choudhary
License: MIT
License-File: LICENSE
Keywords: document,document-intelligence,genai,llm,parser,pdf,pymupdf,rag
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.11
Requires-Dist: liteparse>=2.11.1
Requires-Dist: llama-cloud>=2.13.0
Requires-Dist: openai>=2.53.0
Requires-Dist: pydantic>=2.0
Requires-Dist: pymupdf>=1.26.0
Description-Content-Type: text/markdown

# AI Document Intelligence

A Python SDK for document intelligence and ingestion pipelines for LLM and RAG applications.

The SDK provides a unified pipeline for:

- 📄 Document parsing
- 📑 Page-wise text and Markdown extraction
- 📋 Document metadata
- 🧩 Semantic Markdown-aware chunking
- 🧠 Embedding generation
- 🔗 Standardized document, chunk, and embedding models
- ⚡ Support for multiple document input sources
- 🐍 Strongly typed Pydantic models

The SDK is designed to be **domain-agnostic** and can be used for financial documents, resumes, reports, contracts, and other business documents.

---

## Features

### 📄 Document Parsing

Parse documents using pluggable parser implementations.

Currently supported:

- **LiteParse** — local/free parsing
- **Llama Cloud** — premium cloud-based parsing

The parser layer exposes a unified interface so the rest of the pipeline does not depend on a specific parsing provider.

### 📑 Page-wise Extraction

Parsed documents provide page-level information including:

- Text
- Markdown
- Page number
- Document elements where supported

### 📋 Document Metadata

Standardized document metadata is exposed through Pydantic models.

```python
document.metadata.page_count