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.
CocoIndex V1 is live: a ground-up redesign of incremental data pipelines, built for AI engineers and agent builders shipping RAG, memory, and knowledge graphs.
Build a pipeline that turns YouTube podcasts into a knowledge graph: extract speakers, statements, and entities with an LLM, then dedupe them with embeddings.
How CocoIndex moved from pickle to type-guided serialization that uses Python type hints to pick the right serializer, no decorators or registration needed.
Five patterns for a Python CLI background daemon that auto-starts, upgrades transparently, and shuts down in under a second, from the daemon behind cocoindex-code.
Featuring five new target connectors, filesystem-level change detection, Python 3.14 free-threading, and smarter pipeline lifecycle management.
Featuring production-ready resilience, structured error system, expanded integrations, and always-fresh structured context for agents operating in the real world.
Featuring batching support for CocoIndex functions, execution robustness, schema & type system improvements, custom source support, and more.
CocoIndex now batches GPU and ML workloads automatically: 5x throughput on text embeddings and AI ops, with zero configuration required.
Production-ready upgrades: durable execution, faster incremental processing over large datasets, GPU isolation, and richer native building blocks.
A mental framework for Rust's memory safety concepts. Think systematically about ownership, references, Send, Sync, and Rc, Arc, RefCell, Mutex, etc.
How CocoIndex's layered concurrency controls optimize data-processing performance, prevent system overload, and keep pipelines stable and efficient at scale.