Reference architectures
Each architecture names the slots, the role of each component, honest alternatives (Qdrant, pgvector, S3, Twelve Labs, Mem0, FiftyOne), where Pixeltable fits, and a TableModel sketch. Tutorials stay on /learn/how-to, /use-cases, /recipes, and /examples.
Multimodal RAG reference architecture
Multimodal RAG retrieves passages, stills, or frames, then generates from those rows. The index is one piece. Object storage, a parser, a vector engine, and an LLM API are slots you can keep or fold into one TableModel file.
Video search reference architecture
Video search ranks a moment in a library, not a filename. Sample stills, embed them in a shared text-and-image space, and return the parent clip with a presentation timestamp. A hosted video API is a different product: JSON over HTTP, no table you own.
Agent memory backend reference architecture
Agent memory is a store of facts and preferences the model did not see in the current messages. Embed the rows, recall the ones similar to this turn, and put them in the prompt. Mem0, Letta, and Zep are products for that job. Pixeltable is the table you write instead.
User-uploaded media pipeline reference architecture
User-uploaded media is an HTTP route, a typed row, optional exact-hash and near-duplicate checks, models that run on insert, and an index that stays current. The glue stack is FastAPI plus S3 plus a queue plus a vector database. One TableModel file can cover the same slots.
Robotics episode curation reference architecture
Episode curation keeps robot teleop or autonomy runs as rows: a clip, JSON telemetry, a success flag, sampled stills, detections, and an index so a new run retrieves similar successes and failures. FiftyOne and Rerun are the visualizers. Pixeltable is the production table those exports come from.
Tutorials stay on how-to guides, use cases, and example apps. The interactive agent diagram is /ai-agent-architecture.