Spark-native embedding lifecycle — produce, CDC refresh, migrate, audit.
Project description
drift-spark
Spark-native embedding lifecycle — produce, CDC refresh, model-migrate, audit.
pip install drift-spark · import drift · MIT
Status: pre-alpha (v0.0.1 placeholder).
Drift is a Python library that turns the standard 300-line PySpark embedding pipeline into three declarative commands:
drift embed --table my_catalog.docs --text-col body --sink qdrant://localhost:6333/docs
drift watch --table my_catalog.docs --text-col body --sink qdrant://localhost:6333/docs
drift status --sink qdrant://localhost:6333/docs
What it does:
embed()— Spark-native embedding with dedup, batching, multi-model, Qdrant + pgvector sinkswatch()— incremental CDC refresh via Delta Change Data Feed → only changed rows re-embeddedmigrate()— dual-write model migration with lineage tracking (v1.0); adapter projection (v2.0)- Lineage ledger — per-embedding cost, source tracing, GDPR-delete proof (SQLite, queryable)
GitHub: https://github.com/aayush4vedi/drift-spark PyPI: https://pypi.org/project/drift-spark/0.0.1/
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