Data engine
Transform raw content into structured, AI-ready datasets for training and retrieval.
The data engine transforms raw content into structured, AI-ready data. It manages the complete data lifecycle from file ingestion through preparation for training and retrieval workflows.
Core capabilities
The data engine provides two primary functions:
Storage
Storage manages raw content through file ingestion and vector database creation. Files are uploaded, processed, and organized for downstream use, with ingestion insights that provide real-time visibility into processing status and diagnostics (available through the API and SDK). Vector databases transform these files into searchable knowledge bases through document chunking and embedding generation.
AI-ready data
AI-ready data generates and transforms datasets, converting raw content into training-ready formats. These jobs produce structured outputs optimized for model fine-tuning and alignment, including standard instruction datasets and context-grounded datasets.
Data workflow
The typical data engine workflow:
- Upload raw content files to storage.
- Create vector stores for retrieval applications.
- Generate AI-ready datasets from selected files.
- Use outputs for fine-tuning, agent knowledge bases, or evaluations.
Integration points
Data engine outputs integrate across SeekrFlow:
- Fine-tuning – AI-ready datasets feed model training pipelines
- Agents – Vector stores power FileSearch tool for knowledge retrieval
- Evaluations – Structured datasets support model testing and validation
Updated about 1 month ago