Retrieval-augmented generation pipelines built on fresh, indexed data.
Index a codebase for RAG and AI coding agents with CocoIndex V1 and Tree-sitter: language-aware chunking, embedding, and a live vector index in async Python.
Define query handlers in CocoIndex and trace search results back to source data in CocoInsight to close the loop on indexing strategy.
Build a unified visual document index from multiple file formats (including PDFs, images, and slides) using CocoIndex and ColPali. No OCR needed.
Index academic papers with CocoIndex: read the first page of each PDF, extract title, authors, and abstract with an LLM, and embed them for semantic search.
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.
First release of CocoIndex Changelog: LLM support, codebase indexing, custom functions, and assorted core/performance improvements
Indexing codebase for RAG with CocoIndex and Tree-sitter in real-time: chunking, embedding, semantic search, and build vector index for efficient retrieval.
CocoIndex is now open source: the first engine to combine custom transformation logic with incremental processing built specifically for data indexing.
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.