Customizable Query Routing

via “adaptive-retrieval-with-query-routing”

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

Unique: Implements query-aware routing that dynamically selects retrieval strategies based on query characteristics, allowing different query types to use optimized methods rather than forcing all queries through a single pipeline — an adaptive approach that improves both efficiency and quality

vs others: More efficient than applying all retrieval strategies to every query (fusion) because it selects the most appropriate strategy, and more effective than single-strategy systems because it adapts to query type

via “routing pattern for dynamic task direction based on query classification”

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

Unique: Implements routing as an intelligent classification step that analyzes query characteristics to select specialized handlers, rather than using static rules or random assignment, enabling adaptive pipeline selection based on query semantics.

vs others: More efficient than single-pipeline systems by avoiding unnecessary processing steps, and more adaptive than rule-based routing by using LLM reasoning to classify queries based on semantic content.

MCP server: db-map

Unique: Features a dynamic routing engine that evaluates query characteristics in real-time, allowing for optimized database interactions.

vs others: More flexible than static routing mechanisms, as it adapts to the nature of each query rather than applying a one-size-fits-all approach.

via “customizable routing for ai model requests”

MCP server: keris_edumcp

Unique: Features a highly configurable routing engine that allows for complex decision-making based on request content.

vs others: More adaptable than fixed routing systems, allowing for dynamic changes without redeployment.

via “custom-routing-policy-configuration”