Adaptive Query Routing in Retrieval Augmented Generation
Imagine this scenario:
You're using a Retrieval Augmented Generation (RAG) system for a chat-to-document application, and you’ve come up with a set of questions for testing. For some of the questions, accuracy is paramount—no room for errors. In other cases, you are looking for some more elaboration and interpretation.
The ideal solution should be able to distinguish the nuances of your requirements. Depending on the nature of your query, whether it's a fact-based or seeks summarization and interpretation, the system adapts its retrieval methods to provide you with the most relevant and useful responses.
How to do ?
One of the options is to overlay a classification model to recognize these different intents within your RAG system.
The following is an illustration of a potential solution for query routing with a classifier.
Fact-Based Questions
With an effective query routing is implemented, the RAG system will prioritize retrieval methods that deliver concise and accurate answers when precision is crucial and the user is seeking specific factual information.
This involves scanning summary-level information or direct references to facts within documents. You can get precise answers without the fluff.
Summarization Questions
When you're looking for a comprehensive summary or an in-depth understanding of a topic, the system now knows to shift gears. It navigates through document summaries and delves deeper into text chunks to extract the necessary context, enabling it to generate comprehensive and coherent summaries.
If you're interested more details, the book covers more in-depth. We’ll also talk about these topics in our YouTube channel in the future!
Happy practicing and happy building!
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