PDF Inline Image RAG
БесплатноНе проверенBuilds searchable SQLite databases from PDFs, preserving inline image locations for AI agents to discover and caption visual content. Supports full-text search
Описание
Builds searchable SQLite databases from PDFs, preserving inline image locations for AI agents to discover and caption visual content. Supports full-text search over text, image placeholders, and saved captions.
README
An MCP server and CLI for building local, searchable SQLite databases from PDFs where important content appears inside inline images, figures, diagrams, or scanned image blocks.
The key rule is simple:
- Extract PDF text normally.
- Extract only actual PDF image blocks, not whole-page screenshots.
- Insert image placeholders into the page text stream at their page-flow location.
- Store every extracted image with its exact PDF bounding box.
Example text_with_images marker:
[[IMAGE page=72 index=1 bbox=80.6,76.0,535.7,645.7 size=1896x2373 file=mtp-2_assets/images/page_0072_image_01.png]]
This gives an AI agent enough context to search normal text, notice where an image appeared, fetch the image asset, caption/OCR it, and save the caption back into the searchable index.
Install
pip install git+https://github.com/Joncallim/pdf-inline-image-rag-mcp.git
For local development:
git clone https://github.com/Joncallim/pdf-inline-image-rag-mcp.git
cd pdf-inline-image-rag-mcp
python -m venv .venv
. .venv/bin/activate
pip install -e ".[dev]"
MCP Usage
Add this server to your MCP client:
{
"mcpServers": {
"pdf-inline-image-rag": {
"command": "pdf-inline-image-rag-mcp"
}
}
}
Available tools:
build_pdf_ragsearch_pdf_ragget_pdf_pageget_pdf_imagelist_uncaptioned_pdf_imagessave_pdf_image_captioninspect_pdf_rag
Typical flow:
- Call
build_pdf_ragwith a PDF path and output directory. - Call
search_pdf_ragfor normal text queries. - When a result includes
[[IMAGE ...]], callget_pdf_pageorget_pdf_image. - Caption or OCR the image with your preferred model.
- Call
save_pdf_image_captionso the caption is added to page text and FTS.
CLI Usage
Build a database:
pdf-inline-image-rag build \
--input /path/to/file.pdf \
--output-dir outputs/pdf_rag \
--replace
Build only selected pages:
pdf-inline-image-rag build \
--input /path/to/file.pdf \
--output-dir outputs/pdf_rag \
--pages 1-10,42
Search:
pdf-inline-image-rag search \
--db outputs/pdf_rag/file_rag.sqlite \
"sector method"
Inspect:
pdf-inline-image-rag inspect \
--db outputs/pdf_rag/file_rag.sqlite
Output Layout
outputs/pdf_rag/
file_rag.sqlite
file_rag_export.md
file_assets/
images/page_0001_image_01.png
visual_json/page_0001.visual.json
Whole-page PNG rendering is disabled by default. Use --render-pages only for debugging.
SQLite Tables
pages:
text: normal embedded PDF texttext_with_images: text plus inline image placeholdersmarkdown: page-level retrieval documentimage_countneeds_ocr
images:
file_pathbbox_x0,bbox_y0,bbox_x1,bbox_y1width,heightblock_numberplaceholdercaptioncaption_model
pages_fts:
- FTS5 index over text, image placeholders, markdown, and saved captions.
Notes
This project does not invent image captions. It extracts image blocks and makes them discoverable. Use an OCR or vision model to caption the extracted images, then persist the caption with save_pdf_image_caption.
Установка PDF Inline Image RAG
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/Joncallim/pdf-inline-image-rag-mcpFAQ
PDF Inline Image RAG MCP бесплатный?
Да, PDF Inline Image RAG MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для PDF Inline Image RAG?
Нет, PDF Inline Image RAG работает без API-ключей и переменных окружения.
PDF Inline Image RAG — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить PDF Inline Image RAG в Claude Desktop, Claude Code или Cursor?
Открой PDF Inline Image RAG на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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