Back to directory

ApeRAG

A production-ready retrieval-augmented generation platform that combines graph RAG, vector search, and full-text search with AI agents, MCP support, and Kubernetes deployment for enterprise use.

ApeRAG

ApeRAG is a RAG platform that combines Graph RAG, vector search, and full-text search with AI agents for building applications with hybrid retrieval, multimodal document processing, and enterprise-grade management features over a knowledge base.

It starts through Docker Compose, exposing a web interface and an API with documentation, and supports MCP so AI assistants can browse collections, run hybrid search, and ask natural-language questions against a knowledge base directly, authenticated by an API key passed as a header or environment variable. Document parsing can be extended with an add-on service powered by MinerU for more difficult document layouts.

Kubernetes deployment is documented as the recommended path for production use, alongside development instructions and a Docker image build process.

More in RAG

See all 540
  • ai·rete·rag

    ai-rete-rag.com

    A platform that pairs a Rete rule engine with retrieval-augmented generation, so deterministic rules decide an outcome and grounded retrieval explains the decision in plain language.

  • Xtriever

    github.com

    A hybrid retrieval engine for retrieval-augmented generation, written in Rust to run on-device on a phone or a laptop, combining lexical BM25 search, dense embedding search, reciprocal-rank fusion and cross-encoder re-ranking with no server.

  • FileFlux

    github.com

    A .NET library that turns PDF, DOCX, HWP and other documents into optimized chunks for RAG systems, through a five-stage pipeline built on high-performance Rust FFI readers.