RAG query routing is all about ensuring that when a user asks a question, the right piece of information is retrieved from your knowledge base, and not just any random piece.
Let’s see this in action with a simplified example. Imagine you have two distinct knowledge bases: one for "Product Documentation" and another for "Customer Support FAQs."
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import PydanticSingleSelector
# Load documents
product_docs = SimpleDirectoryReader("product_docs").load_data()
faq_docs = SimpleDirectoryReader("faq_docs").load_data()
# Create indices
product_index = VectorStoreIndex.from_documents(product_docs)
faq_index = VectorStoreIndex.from_documents(faq_docs)
# Create query engines
product_engine = product_index.as_query_engine()
faq_engine = faq_index.as_query_engine()
# Define tools for the router
product_tool = QueryEngineTool(
query_engine=product_engine,
metadata=ToolMetadata(
name="product_documentation",
description="Provides information about product features, specifications, and usage guides."
),
)
faq_tool = QueryEngineTool(
query_engine=faq_engine,
metadata=ToolMetadata(
name="customer_support_faq",
description="Answers frequently asked questions about common customer issues and troubleshooting."
),
)
# Create the query router
query_engine = RouterQueryEngine(
selector=PydanticSingleSelector.from_defaults(),
query_engine_tools=[product_tool, faq_tool],
)
# Example query
response = query_engine.query("How do I reset the password for my account?")
print(response)
When you run this, the RouterQueryEngine will analyze the incoming query ("How do I reset the password for my account?"). It consults the metadata of each QueryEngineTool. In this case, the description for customer_support_faq ("Answers frequently asked questions about common customer issues and troubleshooting") is a much better match for the query than the product_documentation description. The router then dispatches the query only to the faq_engine.
The core problem RAG query routing solves is information overload and irrelevance. Without it, a single, massive vector index might contain everything, leading to diluted search results or long-latency queries as the system sifts through irrelevant data. By segmenting your knowledge into logical, distinct indices (or "tools"), you create specialized "experts" that the router can call upon. Each index is optimized for a specific domain, meaning its embeddings are trained on and represent that domain’s nuances. This allows for more precise retrieval. The RouterQueryEngine acts as an intelligent dispatcher, reading the user’s intent and mapping it to the most appropriate specialized index. It’s not just about where to look, but who to ask.
The magic lies in the RouterQueryEngine’s selector. The default PydanticSingleSelector uses a language model to classify the query and pick the single best tool. It does this by prompting the LLM with the available tool descriptions and the user’s query, asking it to choose the most relevant tool. You can customize this selector, for example, to allow multiple tools to be selected if a query spans across domains, or to use a different LLM for the routing decision itself. The ToolMetadata is crucial here; its description field is what the LLM uses to make its routing decision. The more accurate and descriptive these are, the better the routing will be.
The system doesn’t just blindly send the query. It understands the query’s intent by comparing it against the semantic meaning embedded in the tool descriptions. This understanding is powered by the same LLM that might be used for generating responses, making the routing process context-aware and highly effective. You can even add more complex logic, like setting QueryEngineTool.get_response_synthesizer() to customize how each tool’s response is processed before being returned to the user.
When you have deeply nested or hierarchical data, you might find yourself building multiple layers of routers. A top-level router could direct queries to either a "Sales" index or a "Technical Support" index. Within "Technical Support," a sub-router might then direct queries to "Hardware Issues," "Software Bugs," or "API Problems." This creates a sophisticated, multi-expert system that can handle increasingly complex information landscapes.
The next step in mastering RAG is understanding how to dynamically update these knowledge bases and ensure the router’s routing logic remains accurate as your data evolves.