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Case study

2026

Full-stack

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Confluence RAG Knowledge Base

A retrieval-augmented Q&A system over Confluence that stays fresh automatically.

  • Python
  • PostgreSQL
  • Docker
  • Confluence
case study
Confluence RAG Knowledge Base preview

Summary

Problem
Teams knew the answer was documented in Confluence, but finding and piecing it together across spaces took about 30 minutes.
Approach
A FastAPI service that syncs pages every 24 hours through n8n, detects changes with MD5 checksums so only changed pages are re-embedded, stores vectors in PostgreSQL with pgvector, and answers with OpenAI and LangChain, citing sources. Ships as a Docker Compose stack.
Result
From roughly 30 minutes of searching to a 15-second answer with source links.

Story

Overview

Ask a question in plain language and get an answer grounded in your Confluence pages, with links to the sources it used.

What I built

  • A Confluence client and document processor that chunks pages
  • Change detection with MD5 checksums so daily syncs stay cheap
  • pgvector similarity search inside PostgreSQL
  • A FastAPI query endpoint returning answers with citations

Read the full write-up in the blog post.

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