How I Built an AI Pet Therapist Matching Engine (With Python + Embeddings)

We've all seen apps that recommend movies or music based on your mood. But what about pets ? A few months ago I started asking: what if we could use AI to match people with the right kind of emotional support animal based on their lifestyle, mental health needs, and living situation? The result is MyPetTherapist — and this post breaks down exactly how I built the matching engine. The Problem With Generic Pet Advice Most pet recommendation tools are basically fancy quizzes: Do you have a yard? → Get a dog Live in an apartment? → Get a cat Busy lifestyle? → Get a fish That's surface-level at best. Emotional support animal matching is a different problem entirely. Someone with anxiety doesn't just need a pet — they need the right temperament, energy level, and bonding style for their specific situation. This is where LLMs genuinely shine. Architecture Overview User Input → Embedding Layer → Retrieval (pet profiles DB) → LLM Reasoning → Match Score → Recommendation Three main components: S