routir

Deploy and query retrieval pipelines as a single RoutIR service.

14|1|Updated Oct 10, 2025
One-click install
npx skills add https://github.com/hltcoe/routir --skill routir
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: routir
Source: https://github.com/hltcoe/routir/tree/main
Command: npx skills add https://github.com/hltcoe/routir --skill routir

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires quart, lark, grpcio-tools, and includes scripts (resource) components.

What problem does it solve?

RoutIR removes the friction of serving retrieval models by giving you one service that can handle search, reranking, query expansion, score fusion, and document access behind a consistent API.

Core Features & Use Cases

  • Unified retrieval serving: Host dense, sparse, reranking, fusion, and expander engines behind REST or gRPC.
  • Composable pipelines: Chain stages together with the pipeline DSL to build hybrid and multi-stage RAG retrieval flows.
  • Collection and view support: Serve single-view or multi-view document collections, including text and bytes content for multimodal reranking.
  • Operational deployment: Add authentication, caching, remote service relays, and configurable batching for production use.

Quick Start

Ask RoutIR to start from your JSON config and then query the exposed services with the client or pipeline DSL.

Frequently Asked Questions about routir

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy a RAG retrieval pipeline as a service?

Deploy RAG retrieval pipelines as a service by providing a JSON configuration file to define dense search, sparse retrieval, reranking, and fusion stages. The service exposes these stages behind unified HTTP or gRPC APIs for client access.

What is the best way to combine dense and sparse retrieval for search?

Combine dense and sparse retrieval by using a pipeline DSL to chain search, reranking, query expansion, and score fusion stages. This composable approach builds hybrid multi-stage RAG retrieval flows within a single service.

Can I serve multimodal document collections with text and bytes content?

You can serve single-view or multi-view document collections that include both text and bytes content. This collection support enables multimodal reranking and view-aware content routing through the configured HTTP or gRPC clients.

Does this retrieval service support gRPC and REST APIs?

The retrieval service supports both gRPC and REST APIs. You can query exposed dense search, sparse retrieval, and reranking pipelines using clients that handle batching, caching, and authentication.

How do I add authentication and caching to a production retrieval pipeline?

Add authentication and caching to retrieval pipelines by configuring operational deployment features within your JSON config. This production setup also supports configurable batching and remote service relays for RAG workflows.

When should I use remote service relays in a RAG workflow?

Use remote service relays in RAG workflows when you need to distribute or proxy retrieval tasks across external services. Relays allow your pipeline to integrate remote dense search, sparse retrieval, or reranking engines behind a consistent API.