Build a pipeline that converts YouTube podcasts into a structured knowledge graph — extracting speakers, statements, and entities with LLM, then resolving duplicates with embeddings.
How CocoIndex evolved from pickle to a type-guided serialization system that uses Python type hints to automatically choose the right serializer — no decorators or registration needed.
Patterns for building local daemons that start on first use, upgrade transparently, and shut down cleanly — learned from building cocoindex-code's semantic search daemon.
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.
Learn how CocoIndex's layered concurrency control features help you optimize data processing performance, prevent system overload, and ensure stable, efficient pipelines at scale.