Implementation Guides
Step-by-step guides for building production-ready AI applications with Pixeltable. From a video search engine to RAG pipelines. These are AI automation workflows: insert a row, computed columns run. What that phrase means. Task-grouped notebooks and recipes: /examples. Architectures: /reference-architecture.
Declarative AI Infrastructure: Define Pipelines, Not Plumbing
Replace thousands of lines of orchestration code with declarative computed columns. Pixeltable handles execution, dependencies, stored results, and incremental updates automatically.
Incremental Updates: Stop Reprocessing Entire Datasets
Stop reprocessing entire datasets when one row changes. Pixeltable tracks dependencies and recomputes only what's necessary, saving compute, time, and money.
Video Intelligence Pipeline: Extract, Enrich, and Search Video at Scale
Build an end-to-end video analysis system with Pixeltable. Ingest video, extract frames, run multimodal AI models, generate embeddings, and enable semantic search, all as computed columns on a table.
Build a Video Search Engine in Python
A video search engine finds a moment inside a clip, not only a filename. You sample frames or keyframes, embed them with a model such as CLIP, index those vectors, rank by similarity, and serve the query over HTTP.
Production RAG: From Documents to Answers in One System
Build a complete Retrieval-Augmented Generation pipeline with Pixeltable. Ingest documents, chunk text, generate embeddings, index for retrieval, and generate LLM answers, with no vector database or orchestrator required.
Computer Vision Pipeline: Object Detection, Classification, and Search
Build optimized computer vision workflows with Pixeltable. Run YOLOX, CLIP, and custom models as computed columns with automatic batching, stored results, and incremental processing.
AI Agents & MCP: Give Your Agents Persistent Multimodal Memory
Build AI agents with durable memory and tool calling using Pixeltable. Store conversations, images, and documents as queryable tables, give a Pydantic AI agent read access and a memory table, and call MCP server tools from a computed column.
Multimodal AI Apps: Process Any Data Type in One System
Build applications that work with images, videos, audio, and documents simultaneously. Pixeltable treats all modalities as first-class column types with automatic cross-modal operations.
RAG at Scale: Document Processing, Embeddings, and LLM Generation
Build enterprise-grade RAG systems that handle millions of documents with automatic chunking, embedding synchronization, and LLM-powered answer generation.
Audio Transcription Pipeline with OpenAI Whisper
Transcribe audio files at scale with Pixeltable and OpenAI Whisper. Concurrent, rate-limited requests, retries, and incremental processing.