Vector indexes, similarity search, and the databases that back them.
A multi-format CocoIndex v1 pipeline that ingests SEC filings (TXT, JSON), scrubs PII, extracts topics, and powers hybrid search with Apache Doris.
Extract, embed, and store multimodal PDF elements (text with SentenceTransformers, images with CLIP) for unified semantic search with traceable metadata.
Define query handlers in CocoIndex and trace search results back to source data in CocoInsight to close the loop on indexing strategy.
Use an existing PostgreSQL table as a CocoIndex source: derive fields, embed each row, and store the vectors in Postgres with pgvector, incrementally.
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.
CocoIndex sets up Qdrant collections automatically by inferring the target schema from your indexing flow: no manual config, vector sizes kept in sync.
CocoIndex updates: Amazon S3 as a data source, improved query handling, a standalone runtime mode, and more connector and performance improvements.
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.
Text embeddings 101: what they are, why you chunk and embed, and how to build a semantic search index with CocoIndex v1 and Postgres pgvector.
CocoIndex updates: knowledge graph support, Qdrant and Supabase targets, KTable and LTable data types, additional LLM providers, and more.
Index the documents in a shared Google Drive folder as text embeddings in Postgres with CocoIndex v1, then search them by meaning instead of by filename.
Indexing codebase for RAG with CocoIndex and Tree-sitter in real-time: chunking, embedding, semantic search, and build vector index for efficient retrieval.
What customizable data indexing pipelines are and why custom transformation logic matters, with practical CocoIndex examples.
CocoIndex is a data indexing platform for AI: ingestion, chunking, embedding, and pipeline management for RAG, semantic search, and knowledge graphs.