Indexing and searching images, audio, video, and mixed-media content.
Build a CocoIndex v1 pipeline that turns PDF slides into vision-generated speaker notes, local Pocket TTS narration, and searchable LanceDB records.
Extract Pydantic-typed structured data from patient intake forms using DSPy and CocoIndex: OCR vision models with incremental processing.
Extract, embed, and store multimodal PDF elements (text with SentenceTransformers, images with CLIP) for unified semantic search with traceable metadata.
Build a unified visual document index from multiple file formats (including PDFs, images, and slides) using CocoIndex and ColPali. No OCR needed.
CocoIndex updates: production readiness, scalability, and reliability, plus more customization, native integrations, and multi-modal pipeline features.
Index images with ColPali multi-vector patch embeddings and a Qdrant MaxSim collection using CocoIndex v1: incremental, live, in plain async Python.
CocoIndex natively handles typed multi-dimensional vectors, from simple arrays to multi-vector embeddings, unlocking multimodal AI pipelines at scale.
Build a scalable face detection and recognition pipeline with CocoIndex: embed faces, structure for search, and export to a vector DB.
Search a folder of photos by meaning with CocoIndex v1: CLIP embeds images and text into one vector space, the index runs live inside a FastAPI app, and vectors live in Qdrant.
Extract typed Patient records from PDF and DOCX intake forms with an LLM and CocoIndex v1: the nested schema is the whole prompt; results land in Postgres.