Enhancing Your RAG Pipeline: Adding Semantic Routing for Intent Handling

Behind every successful RAG pipeline is a system that knows just which expert to consult.

In a RAG pipeline, ensuring that queries receive the right responses is crucial. But what happens when the query isn’t just about information retrieval, but about handling specific intents like greetings, code execution or something else? In this blog, we’ll dive into how you can enhance your pipeline to handle these cases more effectively — by adding a semantic router.

NOTE: This is the second part of a previous tutorial where we built a simple RAG pipeline. We’ll be building on that foundation as we explore the next steps. If you haven’t checked it out yet, you can find it here.

Table of Contents

• Introduction to Semantic Routing
• Implementing Semantic Routing
• Integrating Semantic Routing into existing RAG Pipeline
• Conclusion

Introduction to Semantic Routing

In a RAG pipeline, different queries require different approaches. Depending on what the question is asking, the system might need to access various data sources, use specific components, or apply particular prompts to get the best response. Semantic routing helps by directing each query to the right place for the most accurate and relevant answer.

Routing to Different Sources of Data
Sometimes, a query needs to pull from various data sources. For instance, querying a vector database to retrieve similar documents for unstructured data, converting a user query into SQL to fetch structured data, searching the internet for the latest information, or querying an API to get specific data.

Routing to Different Components
Based on the query’s complexity, it might be necessary to route it to different components. For example, a question might require documents from a vector database, the assistance of an agent for complex tasks, or direct handling by the LLM without needing external data.

Routing to Use Different Prompts
In some cases, the nature of the query might require different prompts tailored to specific contexts. For instance, if a RAG pipeline is designed to answer questions over multiple types of magazines — such as finance, fashion, and stock market — semantic routing can first determine the relevant category and then apply the appropriate prompt to the query.

Implementing Semantic Routing

Now that we’ve covered the basics of semantic routing, let’s dive into integrating it into our RAG pipeline. We’ll walk through the key steps, from defining routes and configuring the encoder to implementing the logic that directs queries based on intent. By the end, you’ll have a fully functional semantic router that precisely routes queries.

Before starting, make sure to have semantic-router installed in your environment. Run pip install -qU semantic-router .

Importing the Required Libraries

from semantic_router import Route
from semantic_router import RouteLayer

Defining the routes

greeting_route = Route(
    name="greet_user",
    utterances=[
        "hi how are you",
        "hey whatsupp",
        "good morning",
    ],
)

conclude_route = Route(
    name="conclude_text",
    utterances=[
        "thanks",
        "thats what i wanted!",
        "you are awesome",
        "got it",
    ],
)

routes = [greeting_route, conclude_route]

To start, we’ll define specific routes that correspond to different user intents. Each route will have a unique name and a list of utterances that it recognizes.

Setting Up the Encoder

from semantic_router.encoders import HuggingFaceEncoder

encoder = HuggingFaceEncoder()
encoder.name = "sentence-transformers/all-mpnet-base-v2"

Next, we set up an encoder for our routing layer. This encoder model will be responsible for generating embeddings, which are essential for accurately matching user queries to the appropriate routes. The better the model you choose, the more accurate and effective your results will be.

rl = RouteLayer(encoder=encoder, routes=routes)

This line initializes the routing layer, linking the encoder and the predefined routes to process incoming queries.

Testing Our Router

route = rl("Hey how are you?")
print(route)

Output of above code

By passing a query to the routing layer and printing the result, we can verify that the query is correctly classified and routed.

Integrating Semantic Routing into existing RAG Pipeline

Now that we have our semantic routing set up, the next step is to integrate it into our existing RAG pipeline we built in the previous tutorial.

question = "tell me about encoder and decoder?"
route = rl(question)

if route.name == "greet_user":
    llm_answer = "Hey there! How can I help you today?"
elif route.name == "conclude_text":
    llm_answer = "I am glad I could help! Have a great day!"
else:
    llm_answer = llm(SYSTEM_PROMPT, question, 'gpt-4o')

Depending on the route determined by the router, the query will either trigger a predefined response or be handled by the LLM.

Feel free to experiment by adding more routes and customizing the logic to suit your needs. This flexibility allows you to tailor the routing process to better handle a variety of queries, making your RAG pipeline even more powerful.

Conclusion

In this tutorial, we set up a basic semantic router and integrated it into our RAG pipeline. There are additional features to explore with semantic routing, such as adjusting custom thresholds and training the router. We’ll delve into these advanced topics in future tutorials.