A step-by-step guide to building a FastAPI application that accepts image uploads, stores them in DigitalOcean Spaces, runs vision model inference via the Groq API (using Llama 4 Scout), and persists structured results in MongoDB. Covers MongoDB document schema design with nested inference results, multikey indexes on array fields, dynamic query building with dot notation and $elemMatch for compound array filters, aggregation pipelines for label frequency and average confidence analytics, and FastAPI BackgroundTasks for non-blocking inference processing.
Table of contents
IntroductionKey takeawaysPrerequisitesStep 1: Setting Up the Project and Configuring DigitalOcean SpacesStep 2: Designing the MongoDB Document Schema for Model OutputsStep 3: Wiring the Upload-to-Inference Pipeline with Background TasksStep 4: Building Query and Filtering Endpoints for Model-Generated InsightsStep 5: Testing the Full WorkflowFAQsConclusionSort: