Modular RAG MCP Server

High-performance Python library for data extraction, analysis, conversion and manipulation of PDF and other document formats.

A low-code MCP framework for building complex and innovative RAG pipelines. Combines visual pipeline design with MCP protocol integration for end-to-end RAG — from data ingestion and chunking to retrieval and generation.

AI-powered PDF scientific paper translation with preserved formats, supporting Google/DeepL/Ollama/OpenAI services via CLI/GUI/MCP/Docker/Zotero.

SQL-Driven RAG Engine that automatically builds knowledge graphs during querying, combining SQL query capabilities with Retrieval-Augmented Generation for efficient knowledge retrieval.