Building a Metadata-Aware RAG Chatbot for Household Questions via Local LLM Fine-Tuning
By
Author Torgeir Helgevold
Summary
A personal project describes building a chatbot for household questions (maintenance, appointments, etc.) that uses RAG with a vector database. The key innovation is a pre-processing step that categorizes incoming questions into metadata categories (e.g., pool, car, HVAC, cooking) before querying, narrowing the vector search space to only relevant indexed entries for better retrieval results.
Source
Hacker NewsBuilding a Metadata-Aware RAG Chatbot for Household Questions via Local LLM Fine-Tuningteachmecoolstuff.comKey quotes
· 5 pulledAs 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.
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.
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.
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.
The general idea is that the chatbot will get its household knowle
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