deeplake

Why we included this project

Deeplake puts multimodal data and the vectors built from it in one queryable store, so teams building LLM applications don't have to keep a file store, a vector database, and a training pipeline in sync by hand. Its storage format is tuned for deep-learning workloads, and large datasets can be streamed straight into training runs rather than copied to local disk first. Versioning and lineage track dataset changes the way code is tracked, which matters when a model's behavior depends on exactly which data it saw. The same dataset handles retrieval in LLM apps and model training, and LangChain and LlamaIndex integrations fit teams already on those stacks. If your data moves between search and training without constant reformatting, Deeplake is worth a close look.