Source and target connectors that move data in and out of pipelines.
CocoIndex's first post-v1 releases: stable memoization keys, scheduled live refresh, scoped stats, safer SQL connectors, and more graph and streaming integrations.
Walk through a live CocoIndex pipeline that watches a folder of CSV files and publishes each row as JSON to a Kafka topic incrementally, with no glue code.
Featuring five new target connectors, filesystem-level change detection, Python 3.14 free-threading, and smarter pipeline lifecycle management.
A multi-source pipeline that ingests SEC filings (TXT, JSON, PDF), scrubs PII, extracts topics, and powers hybrid search with CocoIndex + Apache Doris.
Featuring production-ready resilience, structured error system, expanded integrations, and always-fresh structured context for agents operating in the real world.
Build a self-updating knowledge graph from meeting notes: extract decisions, tasks, owners, and relationships from your documents with CocoIndex and an LLM.
Featuring batching support for CocoIndex functions, execution robustness, schema & type system improvements, custom source support, and more.
CocoIndex now supports custom sources: read data from any system and keep it incrementally fresh as knowledge for AI agents.
Production-ready upgrades: durable execution, faster incremental processing over large datasets, GPU isolation, and richer native building blocks.
Extract invoice fields from PDFs in Azure Blob Storage and load them into Snowflake with an incremental CocoIndex + GPT-4o pipeline: open-source unstructured ETL.
CocoIndex updates: production readiness, scalability, and reliability, plus more customization, native integrations, and multi-modal pipeline features.
CocoIndex now supports custom targets. Export indexed data to any destination: a local file, cloud storage, a REST API, or your own bespoke system.
CocoIndex now sets up Qdrant collections automatically by inferring the target schema from your indexing flow: no manual config, vector sizes derived from the embedding model and kept in sync.
Build a real-time knowledge graph with Kuzu as a native CocoIndex target: incremental updates, high-performance graph queries.
CocoIndex updates: Amazon S3 as a data source, improved query handling, a standalone runtime mode, and more connector and performance improvements.
Build a real-time data transformation pipeline with Amazon S3 and SQS using CocoIndex: incremental indexing on object storage with live updates that reprocess only changed files.
CocoIndex updates: knowledge graph support, Qdrant and Supabase targets, KTable and LTable data types, additional LLM providers, and more.
CocoIndex updates: incremental processing with live update mode, evaluation utilities, date/time types, a Google Drive source, and core performance improvements.
CocoIndex continuously watches source changes and applies incremental updates to keep derived data in sync, with low latency and no full reindexing.
Step-by-step tutorial to build text embeddings from Google Drive docs with CocoIndex, including service-account setup, and store them in Postgres for semantic search and RAG.