Why a Self-Hosted Qdrant Memory System Returned Grocery Lists for Project Queries

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A developer built a personal AI memory system using Qdrant, a local embedding model, and automatically generated session notes. When asked about recent project follow-ups, the system returned a grocery list because its wording shared similar vocabulary, producing a high cosine similarity score despite being irrelevant. The author explains that vector search is a nearest-neighbor method that finds mathematically similar text rather than truly remembering information. The experience led to adding domain filters to improve retrieval relevance. The article argues that the perceived memory is only as good as the retrieval quality.

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Tracking UK public tenders after Brexit: Find a Tender vs Contracts Finder

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

Prompt Injection: How to Protect Your AI Automations

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