Semantic Router: A Superfast Decision Layer for LLMs and AI Agents
Overview
Semantic Router is an innovative open-source library designed to enhance the efficiency of large language models (LLMs) and AI agents by providing a rapid semantic routing mechanism. Instead of relying on time-consuming LLM generations for every decision—such as tool selection or intent classification—it leverages the power of semantic vector spaces to make instantaneous routing choices based on the underlying meaning of inputs. This approach significantly speeds up processing, making it ideal for real-time applications like chatbots, multi-agent systems, and intent-based orchestration in AI workflows.
The core idea is simple yet powerful: by embedding utterances or queries into a vector space, the router can compare them against predefined routes using cosine similarity or other metrics, selecting the most relevant path without invoking the full LLM. This not only reduces latency but also improves reliability by avoiding hallucinations or inconsistent LLM outputs in decision-making tasks.
Key Features
- Semantic Routing: Define routes with example utterances, and the system routes inputs to the best match using embeddings. If no strong match is found, it defaults to None, preventing erroneous decisions.
- Multi-Modal Support: Handles text, images, and other data types, enabling applications like image classification routes (e.g., distinguishing 'Shrek' images).
- Encoder Integrations: Compatible with popular embedding models from Cohere, OpenAI, Hugging Face (including local models like Mistral 7B), and FastEmbed for flexible deployment.
- Vector Store Integrations: Seamlessly works with Pinecone and Qdrant for scalable storage and retrieval of route embeddings.
- Dynamic Routes: Unlike static rules, it supports parameter generation and function calling, adapting to complex scenarios.
- Local Execution: Fully offline mode using local encoders and LLMs, outperforming cloud-based GPT-3.5 in benchmarks with models like LlamaCpp.
- Optimization Tools: Features threshold tuning for route accuracy and easy saving/loading of route layers.
- Ecosystem Compatibility: Integrates with LangChain for agent building, and supports hybrid routing for advanced setups.
Getting Started
Installation is straightforward via pip: pip install -qU semantic-router. For local use, add [local] or [hybrid] extras.
Define routes as objects with sample utterances:
from semantic_router import Route
politics = Route(name="politics", utterances=["isn't politics the best thing ever", ...])
chitchat = Route(name="chitchat", utterances=["how's the weather today?", ...])
routes = [politics, chitchat]Initialize an encoder (e.g., OpenAI or Cohere) with API keys, then create a RouteLayer:
from semantic_router.encoders import OpenAIEncoder
from semantic_router.routers import SemanticRouter
encoder = OpenAIEncoder()
rl = SemanticRouter(encoder=encoder, routes=routes)Route a query: rl("don't you love politics?").name returns 'politics'. Unmatched queries return None.
Use Cases
- Chatbot Intent Routing: Direct conversations to specialized prompts (e.g., avoid politics or switch to casual chat).
- AI Agent Tool Selection: Faster decision-making for function calls in multi-tool agents.
- 5G Network Management: As cited in research, enhances LLM-assisted intent detection for orchestration.
- Multi-Modal Applications: Route based on images or audio for versatile AI systems.
Performance and Community
With over 3,000 GitHub stars since its launch, Semantic Router has gained traction for its speed (decisions in milliseconds) and ease of use. It's MIT-licensed, actively maintained, and backed by Aurelio Labs. Community resources include Jupyter notebooks for integrations, an online course, and mentions in IEEE papers and Medium articles. Benchmarks show local models like Gemma2 achieving 10ms response times in real-life scenarios.
For more, explore the documentation or GitHub repo.
