Mastering RAG Chatbots: Semantic Router — User Intents
In my third post in the “Mastering RAG Chatbots” series, we will explore the use of semantic routers for intent classification in Retrieval-Augmented Generation (RAG) chatbot applications.
Building conversational AI applications requires understanding the user’s intent behind their query. A semantic router helps disambiguate intent and route queries appropriately. User intent represents the goal or task the user wants to accomplish — are they looking for instructions, recommendations, or specific product details? Understanding intent is key to providing relevant responses in a RAG application.
A semantic router analyzes queries semantically to classify intent. Based on this, it controls which knowledge resources to retrieve from, which prompts, tools or functions to use for generating a tailored response. Properly routing by intent ensures coherent answers mapped to what the user is truly asking.
While powerful, semantic routing for intent control faces key challenges like determining optimal similarity thresholds, handling no-match cases gracefully, properly accounting for sub-intents and variants, resolving ambiguous utterances across multiple intents, and ongoing maintenance of intent taxonomies and training data as the application evolves. When implemented effectively though, it elevates the user experience by precisely understanding needs.
This post dives into the key advantages that semantic routing provides, tackling key challenges using semantic router for intent classification.
Mapping User Intents to Routes
Building on the “RAG gateway” example from the previous post, we want to define routable intents for our Vacation Recommendations RAG application. The “recommendations” intent indicates the user is looking for vacation suggestions, so we’ll route these queries to retrieve and generate relevant recommendation ideas from our data. For the “how-to” intent, the user is asking for instructions, so we’ll route to access guides and FAQs to provide helpful guidance. The “locality” intent captures when users reference a specific location or ask about nearby options, directing these to geo-based data sources. Finally, a “default” route will handle general queries with no clearly defined intent, attempting to provide a useful response. Defining these intents and their corresponding routing policies allows us to appropriately steer queries to the right knowledge sources and response generation flows within the RAG application.
For the recommendations intent route, we want to ensure any suggestions provided to the user are backed by our own data sources, not just the LLM’s general knowledge. Our goal is to give users the best recommendation responses tailored to our domain. If we don’t have a reliable data source to draw recommendations from, we’ll avoid making generic suggestions and simply state that we don’t have any recommendations available. To achieve this, we’ll define a set of recommendation utterances in a JSON file to accurately identify when a user is seeking a recommendation versus other types of queries. This allows us to precisely route recommendation requests to our curated data pipeline.
{
"name": "recommendations",
"score_threshold": 0.7,
"utterances": [
"Can you recommend a good beach destination for a family vacation?",
"I'm looking for suggestions on romantic getaways for couples.",
"What are some popular outdoor adventure vacation ideas?",
"Do you have any recommendations for budget-friendly vacations?",
"I'd like to plan a trip for my upcoming anniversary. Can you suggest some options?",
"Can you recommend a family-friendly resort with activities for kids?",
"I'm interested in a cultural vacation. What cities would you recommend visiting?",
"Do you have any suggestions for a relaxing spa vacation?",
"I'm looking for vacation ideas that combine hiking and sightseeing.",
"Can you recommend an all-inclusive resort with good reviews?"
]
}Leveraging a semantic router for intent classification offers several key advantages over traditional monolithic prompt approaches. By breaking down long, complex prompts into focused sub-prompts mapped to specific intents, we can improve accuracy and reduce ambiguity. The “divide and conquer” approach allows us to precisely target the language model’s capabilities for each intent type.
Additionally, a router architecture provides flexibility to add, edit or remove intents on-the-fly without disrupting the entire system. We can continuously refine and enhance prompts for different areas as needed. This modularity also enables targeted evaluation and improvement processes — pinpointing strengths and weaknesses across various intents. Over time, this focused iteration promotes consistent progress toward an increasingly robust and capable conversational AI system.
Intent Routing Challenges
Implementing effective intent routing with a semantic router faces several challenges:
- Determining the optimal similarity threshold for each route
- Handling no-match cases
- Sub-intents and Variants
- Ambiguous cross-intent utterances
Let’s explore some strategies to overcome this key intent routing challenges.
Determining Optimal Similarity Threshold & Handling No-Match Cases
To find the optimal similarity thresholds for our intent routes, we build a training set containing relevant user queries labeled with their corresponding intents. Importantly, we also include queries without a matching intent to account for no-match scenarios. The data is split into train and validation sets, ensuring a balanced distribution of intents. We then perform a random search optimization using the training set to identify thresholds that route the validation queries to the correct intent labels with the highest accuracy. This process can be executed whenever adding or editing intents.
Including no-intent queries helps prevent overfitting to only existing intents. It allows the router to appropriately route unmatched cases to a fallback flow. Maintaining a balanced validation set ensures the thresholds are robust across all intent types. This data-driven approach to threshold tuning, combined with graceful no-match handling, enhances the router’s reliability in production settings.
Sub-Intent Variations & Ambiguous Cross Intent
For certain major intents, further granularity may be required through sub-intent classification. In such cases, we can employ an inner router architecture. For example, building on the guardrails router concept from the previous post, we could use it as an inner router for intent classification after the initial gateway routing we used for guardrails. This nested approach allows us to first identify a broad intent category, then dive deeper to disambiguate specific sub-intents within that category using the inner router’s semantic understanding capabilities. The diagram below illustrates this concept of using the guardrails router for an additional sub-intent resolution stage.
Summary
This post explored using a semantic router for intent classification in conversational AI apps like chatbots — a cool concept with great advantages. The router analyzes queries to figure out the user’s intent, then routes it to the proper response pipeline. This allows breaking down complex queries into focused prompts per intent for better accuracy, plus flexibility to easily tweak intents without overhauling everything. The modular setup aids continuous improvement too. But nailing the implementation has challenges, like determining optimal similarity thresholds, gracefully handling no-matches, accounting for sub-intent nuances with nested routers, resolving ambiguities, and enabling ongoing refinement. The post covered strategies for tackling these hurdles to build robust intent routing capabilities that elevate the conversational experience.
