How Actian VectorAI DB Works Best When You Keep Embedding Separate

A developer building a local knowledge base with Actian VectorAI DB found that the real challenge was correctly separating responsibilities across tools. The embedding model (all-MiniLM-L6-v2) converts text into 384-dimension float vectors, while VectorAI DB solely handles vector storage and nearest-neighbor search — it never processes raw text. Automation tool n8n was kept strictly as a trigger layer, connecting inputs like Slack or webhooks to a search API without owning any core logic. Tests on a six-note corpus, including a deliberate off-topic distractor, confirmed that semantically relevant queries consistently returned correct top results using cosine similarity scores. The project, published on GitHub, demonstrates that clearly defining each tool's role is key to avoiding common pitfalls like single-entry test corpora or over-engineered workflow chains.