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jayll1303
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semantic-router

Routes user queries to handlers using semantic vector matching and configurable encoders/indexes.

17|3|Updated Apr 3, 2026
Search Tags:#embeddings#qdrant#pinecone#function-calling#vector-search#semantic-routing#intent-classification
One-click install
npx skills add https://github.com/jayll1303/AIEKit --skill semantic-router
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: semantic-router
Source: https://github.com/jayll1303/AIEKit/tree/main/.kiro/skills/semantic-router
Command: npx skills add https://github.com/jayll1303/AIEKit --skill semantic-router

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Route user queries and agent requests quickly and deterministically using semantic vector matching to avoid slow LLM-only routing, reduce latency, and improve precision for intent classification and tool invocation.

Core Features & Use Cases

  • High-speed intent classification: Match queries to predefined routes using embedding similarity instead of expensive generation.
  • Dynamic function calling: Extract parameters and generate function-call schemas for executing tasks programmatically.
  • Flexible index and encoder support: Work with local encoders or API encoders and backends like Local, Pinecone, Qdrant, or Postgres for production.
  • Use Case: Build a chatbot routing layer that identifies user intent, blocks sensitive topics, and triggers appropriate backend functions with extracted parameters.

Quick Start

Use the semantic-router to classify an input query and return the best-matching route name plus any function call arguments.

Frequently Asked Questions about semantic-router

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

FAQPage Schema
How does semantic routing improve intent classification for LLMs?▼

Semantic routing improves intent classification by matching user queries to predefined routes using embedding similarity, bypassing slow LLM generation to reduce latency and increase decision speed.

Can I use Pinecone or Qdrant for vector search in semantic routing?▼

Yes, you can use Pinecone or Qdrant for vector search. Semantic routing supports flexible indexes including Local, Pinecone, Qdrant, and Postgres for production deployments.

How do I route user queries to dynamic function calls using embeddings?▼

You route user queries to dynamic function calls by applying semantic vector matching to extract parameters and generate function-call schemas for executing tasks programmatically.

What encoders work with semantic routing for fast tool routing?▼

Fast tool routing works with multiple encoders including OpenAI, Cohere, HuggingFace, and FastEmbed, allowing you to choose between local or API-based embedding generation.

When should I use semantic vector matching instead of LLM-only routing?▼

Use semantic vector matching instead of LLM-only routing when you need high-speed, deterministic decision-making to avoid generation latency and improve precision for tool invocation.

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