Generating, storing, and serving text and multimodal embeddings.
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.
A multi-format CocoIndex v1 pipeline that ingests SEC filings (TXT, JSON), scrubs PII, extracts topics, and powers hybrid search with Apache Doris.
CocoIndex now batches GPU and ML workloads automatically: 5x throughput on text embeddings and AI ops, with zero configuration required.
Extract, embed, and store multimodal PDF elements (text with SentenceTransformers, images with CLIP) for unified semantic search with traceable metadata.
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.
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.
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.
CocoIndex updates: in-process setup/drop API, EmbedText building block, SplitRecursively improvements, union/NumPy types, and the Kuzu graph target.
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.
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.