Semantic Routing
Learn about routing and ways to implement routing, specifically semantic routing and its step-by-step implementation.
We'll cover the following
LLMs handling diverse user queries require efficient routing mechanisms. Imagine a single LLM trained on a massive dataset covering various domains like finance, health, literature, and travel. While the LLM can access this information, directly feeding a user query might not always lead to the most relevant response.
Routing helps us bridge this gap by directing user queries to specific sub-models or prompts that are best equipped to handle them. This ensures a more focused and informative response for the user.
What is routing?
Routing, in the context of LLMs, is the process of directing a user query to the most appropriate sub-model or prompt within the larger LLM architecture. This sub-model or prompt is likely to have been trained on a specific domain or task, allowing it to generate a more accurate and relevant response.
There are several ways to implement routing in LLMs. We will explore two common methods:
Semantic routing: This method leverages semantic similarity between the user query and pre-defined sets of questions or prompts from different domains.
Routing with LLM-based classifier: Here, a separate LLM classifier is trained to categorize the user query into a specific domain before routing it to the corresponding sub-model.
Semantic routing
Semantic routing is a data-driven approach that utilizes the semantic similarity between the user query and pre-defined prompts or questions from various domains. Here’s a breakdown of how it works:
Pre-defined prompts and questions: We define sets of questions or prompts specific to each domain we want to handle. For example, we might have a set of questions related to personal finance, another for book reviews, and so on.
Embedding user query and prompts: We use an embedding model to convert the user query and pre-defined prompts from each domain into numerical representations. These embeddings capture the semantic meaning of the text.
Similarity calculation: We calculate the cosine similarity between the user query embedding and the embeddings of each pre-defined prompt set. Cosine similarity measures how similar two vectors are in a high-dimensional space.
Routing based on highest similarity: The user query is routed to the domain with the highest cosine similarity. This indicates the closest semantic match between the query and the prompts from that domain.
Prompt selection and response generation: The LLM uses the selected domain-specific prompt along with the user query to generate the final response.
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