The Problem
Keyword search is useful but limited when a question and its answer use different wording. Private knowledge tools also need to keep sensitive source material under direct control.
The Build
The experiment uses a self-hosted vector database with named embeddings and cosine similarity. It provides a retrieval layer that other local tools can query before generating an answer, while keeping the data path inside the private environment.
Architecture
A deliberately simplified public view. Operational addresses, credentials, firewall rules, and recovery details are excluded.
Challenges
- Keeping embedding configuration consistent across ingestion and search.
- Distinguishing retrieval quality from fluent model output.
- Managing stale or duplicate knowledge chunks.
- Preserving private data boundaries when connecting agent tools.
What I Learned
RAG is a pipeline, not a magic feature. Source quality, chunking, metadata, embedding consistency, retrieval inspection, and answer grounding all affect the result.
Current Status
The vector service and collection are operational; the broader knowledge experience remains experimental and private.
Next Step
Add repeatable retrieval evaluations, clearer source metadata, and a sanitized demonstration that reveals no private documents.
Evidence
Public evidence grows with the project.
Repositories, diagrams, screenshots, and demonstrations are linked only when they are accurate, safe, and ready for public use.
Browse Xander Cyber Lab on GitHub ↗Public safety note: This case study is architectural and educational. Sensitive topology, authentication, credentials, and production configuration remain private.