PDF and Document Parsers for RAG

OpenDataLoader PDF is a PDF parser for developers building document search and retrieval-augmented generation systems. It preserves document structure and source locations so applications can retrieve passages and point readers back to the original page.

xberg, formerly Kreuzberg, is a local document extraction engine for developers building AI search and document processing applications. It turns files into text and structured content that those applications can use.

Unstract is a self-hosted, no-code platform for teams that need structured data from documents whose layouts vary. It uses LLMs to extract JSON from PDFs, images, scans and office files without building a template for each vendor or form. Its use cases include finance, insurance, healthcare and KYC document processing.

Nougat is a local AI PDF parser for researchers and developers who need to extract content from academic papers. It handles LaTeX math and tables alongside the paper's text.

LightRAG is a self-hosted framework for developers building AI assistants that answer questions across documents. It connects related facts across files, including material where relationships matter, such as legal and financial records.

FlyMD is a Markdown editor for people who keep notes, write long documents or publish blogs. It stores documents locally and adds writing assistance and document tools through extensions.

DeepSeek-OCR is an OCR model for developers processing document images and PDFs on their own hardware. It also supports research into visual text compression, representing text-bearing images with compact sets of vision tokens.

docTR reads text from PDFs and images for developers building document processing applications and researchers comparing OCR models. It locates individual words and recognizes their text on your own hardware.

RAG-Anything is a Python framework for developers building question-answering systems over documents that mix prose with visual or structured content. Built on LightRAG, it suits research papers, technical manuals and financial reports where answers depend on figures, tables or formulas.

olmOCR converts scanned and image-based documents into text for researchers and developers preparing LLM datasets. It handles pages whose layout makes ordinary text extraction difficult.

ContextGem is a Python framework for developers extracting structured information from documents such as contracts, invoices and reports. You describe what you need in natural language, and it handles extraction prompts and data modelling for text and image content.

MonkeyOCR is a local AI document parser for developers and researchers who need to extract content from English and Chinese PDFs or images. It recognizes page structure and reading order alongside the content itself.

LlamaIndex is a Python framework for developers building AI agents and applications that answer questions using their own data. It provides the connections and retrieval tools needed to give a language model relevant context from documents and other sources.

Marker is a local document converter for developers and teams preparing PDFs, scans and Office files for document processing or AI workflows. It extracts text while retaining structure such as tables, equations and page layout.

Docling turns documents into structured content for developers building search, retrieval and AI applications. It handles page structure and scanned text so applications can work with more than a file's plain text.

Flexible GraphRAG is a self-hosted platform for teams building document search and AI chat that use both text and relationships between entities. It extracts knowledge graphs from documents and combines graph retrieval with vector similarity and full-text search. You can run it standalone or in Docker. It's open source under Apache 2.0.

Chonkie is a library for developers preparing documents for retrieval-augmented generation (RAG). It brings document preparation and ingestion into one pipeline, with optional components to keep dependencies small.

GROBID turns scientific and technical PDFs into structured TEI XML on your own machine or server. It's for researchers and developers who need article metadata, full text and bibliographical references for search, analysis or document collections. The software is open source under Apache 2.0.

RocketRide is an AI pipeline builder and runtime for developers creating document processing, search, and agent workflows. Its visual editor sits in VS Code, alongside the application code that uses those workflows.

PaddleOCR is an OCR and document parsing toolkit for developers turning PDFs and images into data for document search, RAG systems and AI agents. Its local models cover both structured pages and text found in photographs.

Unlimited-OCR is an OCR model for developers building document extraction workflows on their own hardware or servers. It handles individual document images and multi-page inputs, including PDFs converted to page images.

Dolphin Document Parser converts PDFs and document images into structured content for developers working on document extraction. It handles digitally created and photographed pages, including documents that mix text with tables, formulas, and code.

MDFlux is a desktop app for turning documents into structured Markdown for AI workflows, research, or text editing. It suits people who need usable text from mixed files and scanned archives while keeping conversion on their own machine.

Bisheng is a self-hosted AI application platform for teams working with business documents and processes. It supports tasks such as document review, support ticket assistance and fixed-layout report generation, including workflows that need human judgment during execution.

MinerU is a local document parser for people preparing files for AI workflows. It handles scanned PDFs, academic papers and other documents containing OCR text, tables, figures or complex layouts.

TaxHacker is a self-hosted AI accounting app for freelancers, indie hackers, and small businesses that need to turn receipts and invoices into searchable transactions. The project is in early development. Its open source edition runs on your own infrastructure or home server through Docker and uses the MIT license.

dots.ocr is a self-hosted OCR model for developers and teams that need text and page structure from multilingual documents. It combines text recognition and layout parsing in a single vision-language model you can run on your own hardware.

Gitingest prepares code context for developers who want to give an LLM a repository or local directory. It gathers the project structure and file contents into a text digest instead of requiring you to assemble files by hand.

QAnything is a local knowledge base for people and teams who want to ask questions about their own documents. It supports offline operation and questions across Chinese and English document collections.

ExtractThinker is a Python library for developers who want to extract structured data from documents using models running locally through Ollama. It also supports OpenAI, Anthropic and Groq. With a cloud provider, extraction sends document content to that service, requires provider credentials and may incur charges.

llmware is a Python framework for developers building applications that answer questions using their own documents or carry out multi-step AI agent workflows. It focuses on small, specialized models that can run on laptops, edge devices, and self-hosted servers.

PageIndex is a document retrieval engine for developers building question answering over long PDFs, including financial reports, legal documents and technical manuals. It uses an LLM to select relevant sections from a document tree without a vector database or document chunking.

Data-Juicer is a Python framework for researchers and teams preparing datasets for AI training, evaluation, agents or document retrieval. It processes data on your own machine or across a Ray cluster.

RAGFlow helps teams build AI agents that answer questions using their own documents. It extracts content from complex files and retrieves supporting material that readers can check.