RAG Server FAQs

Question 1

Does RAG Server support large-scale indexing and high availability?

Accepted Answer

Yes, RAG Server is designed for scalability and high availability. It features vector indexing with FLAT/HNSW algorithms, asynchronous indexing using Redis Streams for distributed task processing, and robust support for Redis Sentinel/Cluster modes.

Question 2

How does RAG Server improve retrieval accuracy?

Accepted Answer

It enhances retrieval accuracy through multiple methods: it supports both semantic (vector) and hybrid (BM25 full-text + vector) search, and integrates with powerful reranking models like DashScope qwen3-rerank to re-order search results for optimal relevance.

Question 3

What document types and chunking strategies does RAG Server support?

Accepted Answer

RAG Server supports structure-aware document chunking for various formats including Markdown, HTML, plain Text, and PDF files. This intelligent chunking ensures contextually relevant document segments for retrieval.

Question 4

What is RAG Server?

Accepted Answer

RAG Server is a powerful tool built on Redis Stack that provides robust Retrieval Augmented Generation (RAG) capabilities for AI applications. It offers end-to-end solutions for document indexing, semantic retrieval, reranking, and constructing RAG prompts via the Model Context Protocol.

Question 5

What is the Model Context Protocol (MCP) and why is it important for RAG Server?

Accepted Answer

The Model Context Protocol (MCP) is a standardized interface that RAG Server uses to expose its capabilities. It provides a Streamable HTTP API for AI applications to seamlessly integrate with RAG Server's indexing, retrieval, and prompt construction tools, simplifying development and ensuring interoperability.

RAG Server

RAG Server

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