Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
<p>As a fun personal project, I have been working on a chatbot for answering general questions about my household on anything from maintenance questions to doctor’s appointments. </p>
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The general idea is that the chatbot will get its household knowledge through RAG from querying a vector database, but for better results I have made the vector searches metadata aware.
</p>
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Basically, I am running questions through a pre-processing step to categorize questions into known metadata categories (e.g. pool, car, hvac, cooking). The main goal of this is to narrow down the search space for vector ranking to only indexed entries that match the category of the question. As an example, the question “When did we replace our pool pump?” will be mapped to a category called “pool” before querying the Index database.
</p>
<p>
The general idea is that the chatbot will get its household knowledge through RAG from querying a vector database, but for better results I have made the vector searches metadata aware.
</p>
<p>
Basically, I am running questions through a pre-processing step to categorize questions into known metadata categories (e.g. pool, car, hvac, cooking). The main goal of this is to narrow down the search space for vector ranking to only indexed entries that match the category of the question. As an example, the question “When did we replace our pool pump?” will be mapped to a category called “pool” before querying the Index database.
</p>
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