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

Key Features

Use Cases

Key Features

Use Cases