# RAG Chunking, Corpus Cleanup, and Retrieval Prep Tools

Compare chunk-size scoring, text cleanup, OCR recovery, citation-ready packaging, and token planning tools in one hub for building higher-quality RAG collections.

> Canonical page: https://elysiatools.com/en/hubs/rag-chunking-retrieval-prep

- **Category:** prepare

- **Keywords:** rag chunking tools, retrieval prep, knowledge base cleanup, citation-ready chunks, token planning for rag, document chunk quality, rag corpus prep, semantic retrieval workflow

## Overview

This hub focuses on the preparation work that happens before a document collection is trustworthy for retrieval. It brings together source cleanup, page-range isolation, OCR fallback, structure checks, chunk-quality scoring, citation-ready packaging, and token estimation so you can move from raw files to a cleaner, more debuggable RAG corpus.

## Tools

- AI RAG Chunk Quality Scorer: Score candidate RAG chunk-splitting schemes for a document across four metrics — coherence (clean sentence/paragraph boundaries), coverage (topic focus / key-term concentration), context overlap health, and chunk-size consistency — then compare up to three schemes side by side and recommend the best. Pure offline heuristics, no model calls.
- PDF RAG Chunker & Citation Pack: Convert a PDF into heading-aware RAG chunks with page numbers, bounding boxes, and citation metadata
- PDF to Clean Text for LLM: Extract clean text from PDFs with OpenDataLoader for summarization, translation, embedding, and other LLM workflows
- PDF Header/Footer Noise Remover: Compare extraction with and without repeated page furniture to spot header/footer noise before using PDF text in RAG, summarization, or editing workflows
- Tagged PDF Inspector: Compare StructTree-enabled and plain PDF extraction to see whether a document behaves like a tagged PDF and how much semantic structure it exposes
- PDF Prompt Injection Scanner: Compare safe and unsafe PDF extraction runs to detect hidden text, off-page content, tiny text, and hidden-layer prompt injection risks
- PDF Page Range Extractor: Extract only selected PDF pages with OpenDataLoader and export the subset as Markdown, JSON, or text
- Word Text Extractor: Extract text content from Word documents with support for formatting options, paragraph selection, and multi-language processing
- AI Token Estimator: Analyze language mix and estimate token usage across OpenAI, Codex, Claude, and DeepSeek profiles
- Sentence Splitter: Split paragraphs into sentences by punctuation marks (period, question mark, exclamation mark, etc.)
- Text Splitter: Split text by custom delimiters, characters, or patterns
- Scanned PDF OCR to Markdown: Convert scanned or image-heavy PDFs into Markdown with OpenDataLoader hybrid OCR, with a graceful fallback when the hybrid backend is unavailable

## Samples

- Vector Database Samples: Comprehensive vector database examples including Chroma, Pinecone, Weaviate, FAISS, and custom vector solutions for semantic search and RAG applications
- LangChain Samples: AI application development framework examples using LangChain for building LLM-powered applications with chains, agents, and memory
- Markdown Samples: Markdown format examples from simple to complex document structures

## Frequently asked questions

### What can I do in this hub?

You can clean raw documents, inspect structure, extract only the pages you need, prototype chunk boundaries, generate citation-ready packs, and estimate token size before content reaches a vector index.

### Who is this hub for?

It is useful for RAG builders, internal knowledge-base teams, AI engineers, technical writers, and anyone preparing long documents for search, chat, or answer-grounding workflows.

### How should I start?

Start by cleaning or isolating the source document, then evaluate chunk sizes on representative text, and only after that move into citation-ready chunk export or vector-database integration.

## Related content

- [PDF to LLM and RAG Preparation Tools](https://elysiatools.com/en/hubs/pdf-llm-rag-prep): Prepare PDFs for AI workflows by extracting clean text, structured Markdown and JSON, tables, OCR layers, chunk packs, and safety review signals before indexing or prompting.
- [Document OCR and Structured Extraction Tools](https://elysiatools.com/en/hubs/document-ocr-extraction): Extract text, Markdown, JSON, tables, captions, and RAG-ready chunks from scanned PDFs and document images with OCR and structure-aware workflows.
- [Prompt Engineering and LLM Input Preparation Tools](https://elysiatools.com/en/hubs/prompt-engineering-llm-input-workflows): Structure prompts, estimate tokens across OpenAI, Claude, Codex, and DeepSeek, translate prompts, clean PDFs for grounding, and review prompt-injection risk in one prompt engineering hub.
- [PDF Extraction Debugging and Safety Review Tools](https://elysiatools.com/en/hubs/pdf-extraction-debugging-workflows): Inspect reading order, header/footer noise, hidden text risk, OCR fallback needs, and structured export quality in one PDF extraction debugging hub.