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RAG In development

HOA Knowledge Assistant

A retrieval-based assistant that answers residents' questions from a community association's own website content, with sources.

Context
Personal project
My role
Design and build
Code
Private, walkthrough on request
  • Python
  • WordPress REST API
  • Embeddings
  • Vector database
  • RAG

Pipeline

  1. 01 WordPress API Pages, posts, documents
  2. 02 Chunk + embed Vector database
  3. 03 Retrieve Top matching passages
  4. 04 Answer Grounded, with sources

The problem

A homeowners association website holds years of rules, guidelines, meeting notes, and announcements. Residents can’t find the one paragraph that answers “Can I install a fence?” and end up emailing the board.

How it works

Content is pulled through the WordPress REST API, cleaned, split into passages, and embedded into a vector database. A question is embedded the same way, the closest passages are retrieved, and the model answers using only those passages, linking to the source pages.

I prototyped the knowledge base in NotebookLM first to check that the source content could answer real questions before building the pipeline.

How I test it

  • A set of real resident questions with known answers and known source pages.
  • Retrieval check: does the right page appear in the top results?
  • Grounding check: is every claim in the answer supported by a retrieved passage?
  • Refusal check: for questions the site doesn’t cover, the assistant should say so instead of guessing.