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Daft

Multimodal data engine built for AI

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Daft is an open-source multimodal data engine for AI workloads, built to take raw data through to training-ready datasets in a single pipeline. It processes video, images, audio, text, and sensor data alongside structured metadata in one dataframe, addressing the need to handle mixed-modality data at scale without separate tooling.

Daft offers a Python dataframe API with familiar operations such as filter, transform, aggregate, and write, plus SQL support. It runs CPU and GPU work in one pipeline, with scheduling and batching handled automatically. Native model operators cover embeddings, LLM extraction, and structured outputs, with support for models from OpenAI, Hugging Face, or custom providers. Multimodal column types, a managed UDF runtime with automatic batching and retries, and zero-copy execution powered by Apache Arrow are included. The core is written in Rust, and pipelines defined once can run locally or across distributed clusters. Integrations include Apache Hudi, Apache Iceberg, Delta Lake, AWS, Azure, Google Cloud, Unity Catalog, Ray, Pandas, and PyTorch.

Daft is released under the Apache 2.0 license and is installed via pip. It is suited to teams building AI search, data enrichment, and multimodal ETL pipelines. The pages do not specify a free plan, trial, or edition structure.

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