Neural search for refinery. Manages similarity search powered by Qdrant and outlier detection, both based on vector representations of the project records.
Weak supervision for refinery. Manages the integration of heuristics such as labeling functions, active learners or zero-shot classifiers. Uses the weak-nlp library for the actual integration logic and algorithms.
Tokenizer for refinery. Manages the creation and storage of spaCy tokens for text-based record attributes and supports multiple language models. It is used by the gateway.
Embedder for refinery. Manages the creation of document- and token-level embeddings using the embedders library.
Gateway for refinery. Manages incoming requests and holds the workflow logic. To interact with the gateway, the UI or Python SDK can be used.
Execution environment for the active learning module in refinery. Containerized function as a service to build active learning models using scikit-learn and sequence-learn.
Execution environment for labeling functions in refinery. Containerized function as a service to execute user-defined Python scripts.
Execution environment for attribute calculation in refinery. Containerized function as a service to build custom attributes derived from the original data.
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