Source and target connectors that move data in and out of pipelines.
Changelog for CocoIndex 1.0.8-1.0.16: persistent per-component state, LiveMap, rate limiting, batched target writes, BigQuery and Snowflake connectors.
CocoIndex's first post-v1 releases: stable memoization keys, scheduled live refresh, scoped stats, safer SQL connectors, and more 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-format CocoIndex v1 pipeline that ingests SEC filings (TXT, JSON), scrubs PII, extracts topics, and powers hybrid search with Apache Doris.
CocoIndex updates: production-ready resilience, a structured error system, expanded integrations, and always-fresh context for agents.
Build a self-updating Neo4j knowledge graph from meeting notes with CocoIndex v1: LLM-extracted decisions, tasks, and owners, with embedding-based dedupe.
Featuring batching support for CocoIndex functions, execution robustness, schema & type system improvements, custom source support, and more.
Read data from any system with CocoIndex: in v1 a custom source is plain async Python plus processing components. No connector API to implement.
Production-ready upgrades: durable execution, faster incremental processing over large datasets, GPU isolation, and richer native building blocks.
Incremental ETL from Azure Blob Storage to Snowflake with CocoIndex v1: LLM invoice extraction from PDFs, processing only new or changed files.
CocoIndex updates: production readiness, scalability, and reliability, plus more customization, native integrations, and multi-modal pipeline features.
Export CocoIndex data anywhere: implement a TargetHandler with a tracking record and an action sink; the engine handles diffing, syncing, and cleanup.
CocoIndex sets up Qdrant collections automatically by inferring the target schema from your indexing flow: no manual config, vector sizes 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.
Index Markdown from Amazon S3 into Postgres pgvector with CocoIndex v1: incremental processing re-embeds only the files and chunks that changed.
CocoIndex updates: knowledge graph support, Qdrant and Supabase targets, KTable and LTable data types, additional LLM providers, and more.
CocoIndex updates: incremental 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.
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.