Open Source AI Project


Semantic Router is an ultra-fast decision layer for LLMs (Language Models) and Agents, leveraging the capabilities of semantic vector spaces for decision-making.


The Semantic Router project is designed to serve as an advanced decision-making framework specifically tailored for Language Models (LLMs) and Agents. At its core, the Semantic Router utilizes the concept of semantic vector spaces, which are mathematical representations of meanings in a high-dimensional space. These representations allow the Semantic Router to understand and interpret the semantic meaning of requests made to it.

In practical terms, the Semantic Router operates by analyzing the semantic content of incoming requests. It doesn’t just look at the superficial aspects of a request (such as keywords) but delves deeper into the underlying meaning. This enables it to make highly informed decisions about how to route these requests to the most appropriate decision objects.

A “decision object” in this context refers to any entity or module within the system that is designed to take action based on the request it receives. The Semantic Router supports the definition and integration of various decision objects, allowing for a highly customizable and flexible system architecture. This means that depending on the nature and requirements of a request, it can be routed to different decision objects that are best suited to handle it.

To facilitate this semantic-based routing and decision-making, the Semantic Router employs encoder models. Encoder models are a type of neural network designed to transform natural language into vector representations. By using these models, the Semantic Router can convert the semantic information contained in requests into a format that it can analyze and understand. This process is what enables the Semantic Router to make decisions based on the semantic meaning of requests rather than just their literal content.

Overall, the Semantic Router represents a sophisticated approach to decision-making in systems utilizing Language Models and Agents. By leveraging semantic vector spaces and encoder models, it offers a way to route requests and make decisions in a manner that is deeply aligned with the semantic intentions behind those requests, leading to more accurate, efficient, and relevant outcomes.

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