Semantic Routing is an approach to directing inputs (like text, images, or audio) to the appropriate handlers based on their meaning rather than rigid keyword matching or rule-based systems. It provides a more flexible and human-like understanding of content, allowing systems to gracefully handle the natural variability in how people express similar ideas.
Semantic space is a high-dimensional mathematical space where meaning is represented geometrically. Imagine a vast coordinate system where every point corresponds to a specific concept or idea. In this space, the sentence “I need help with my password” exists as a point near “Can’t log in to my account” but far from “What’s the weather forecast?” Semantic similarity becomes a measurable distance, transforming abstract meaning into computable relationships.
This approach allows us to capture the nuanced relationships between concepts, accommodating synonyms, paraphrases, and related ideas without explicitly programming each variation.
To place content in a semantic space, we need to convert it into vector representations – a process called encoding. This is handled by neural network models called bi-encoders (commonly known as embedding models or encoders) which:
For example, the sentence “How’s the weather today?” might be encoded as a vector like [0.12, -0.34, 0.56, ...]
, while “What’s the temperature outside?” would produce a different but nearby vector, reflecting their similar meanings.
Semantic Router supports various encoder types:
OpenAIEncoder
or HuggingFaceEncoder
): Generate vectors where every dimension has a value, capturing complex semantic relationshipsAurelioSparseEncoder
or BM25Encoder
): Generate vectors where most dimensions are zero, excelling at keyword matching and term frequencyWhile text is the most common application, semantic routing works with any content that can be meaningfully encoded into vectors:
CLIPEncoder
or VitEncoder
, images can be placed in the same semantic space as text, enabling cross-modal comparisons and routingThis multimodal capability enables powerful applications like routing based on the content of images, or understanding the combined meaning of text and images together.
Once content is encoded into vectors, semantic similarity is used to make routing decisions. This is typically calculated using mathematical operations like:
Semantic Router compares incoming queries against predefined routes, each represented by one or more example utterances. The route with the highest similarity score above a configurable threshold is selected as the match.
Semantic Router makes implementing these capabilities straightforward:
The library handles the complex vector operations, similarity calculations, and decision-making process, allowing you to focus on defining meaningful routes and creating effective handlers for each case.