ApeRAG

ApeRAG – A Production‑Ready RAG Platform for Intelligent AI Workflows

ApeRAG addresses the growing need for scalable, hybrid retrieval systems that can power advanced AI assistants. Traditional Retrieval‑Augmented Generation (RAG) solutions often rely on a single search modality—either vector similarity or keyword matching—leading to brittle performance when dealing with complex, multimodal knowledge bases. ApeRAG unifies graph RAG, vector search, and full‑text search into a single, cohesive engine. This hybrid approach allows assistants to traverse relationships in a knowledge graph while also leveraging semantic embeddings and keyword relevance, resulting in richer, more accurate responses for end‑users.

For developers building AI agents that must browse, reason, and act on enterprise data, ApeRAG offers a robust set of capabilities. The MCP (Model Context Protocol) integration exposes a standardized API that lets assistants list collections, perform hybrid queries, and retrieve structured results without custom connectors. The server’s web interface provides intuitive exploration of collections and visualizations of graph relationships, while the RESTful API is fully documented for seamless integration into existing pipelines. By exposing these endpoints through MCP, developers can embed the entire knowledge‑retrieval workflow directly into their AI assistants’ context management logic.

Key features include:

• Hybrid Retrieval Engine: Simultaneous vector, full‑text, and graph queries with configurable weighting, enabling nuanced answer generation.
• Advanced Document Parsing: Integration with MinerU’s Docray service for robust extraction of tables, formulas, and complex layouts, ensuring high‑quality embeddings.
• Enterprise‑Grade Management: Role‑based access controls, API key authentication, and scalable Kubernetes deployment support production workloads.
• Intelligent Agent Support: Built‑in agent orchestration lets assistants autonomously decide when to search, re‑rank results, or request clarifications.
• Multimodal Processing: Image and PDF ingestion pipelines allow agents to pull context from visual documents, expanding the range of supported queries.

Real‑world scenarios that benefit from ApeRAG include:

• Customer support bots that need to pull policy documents, product specs, and knowledge‑graph relationships in a single query.
• Enterprise analytics assistants that combine structured database insights with unstructured reports, providing holistic answers to business questions.
• Research assistants that can navigate citation graphs while also retrieving semantic embeddings from scientific papers, enabling deeper literature reviews.

By integrating ApeRAG into an AI workflow through MCP, developers gain a single point of contact for all retrieval needs. The assistant can request a collection list, issue a hybrid query, and receive ranked results—all within the same conversational context—streamlining development and improving user experience. The platform’s modular architecture, coupled with Kubernetes‑ready deployment options, ensures that it can scale from prototype to production without sacrificing performance or reliability.