I wanted a structured way to take raw documents, notes, and artifacts and turn them into usable system knowledge, not just stored files.
Most ingestion systems just store and embed documents. This pipeline is designed to process, structure, store, and track knowledge artifacts so they can be used by agents and systems later with traceability and context.
The goal is repeatable, inspectable knowledge ingestion — not just file parsing.
A multi-stage knowledge ingestion pipeline that processes input artifacts, extracts and structures information, stores it, and generates artifacts and telemetry for each run.
It includes:
- Pipeline orchestration
- Structured data models
- Storage layer
- Context storage
- Artifact generation
- Telemetry and run tracking
- CLI interface for running ingestion jobs
This is infrastructure for knowledge ingestion, not just document processing.
This project demonstrates how to design ingestion systems with:
- Multi-stage pipelines instead of single scripts
- Structured models instead of loose JSON
- Separation of pipeline, storage, and models
- Artifact generation for reproducibility
- Telemetry for observability
- CLI interface for controlled execution
The focus is on building a repeatable ingestion system, not a one-off script.
python cli.pyPlace files in sample_inputs/ and run the CLI to execute the ingestion pipeline.
knowledge-ingestion-engine/
├── cli.py # Command-line interface
├── pipeline.py # Pipeline orchestration
├── config.py # Configuration
├── stages/ # Processing stages
├── models/ # Data models
├── store/ # Storage layer
├── context_store/ # Context data
├── sample_inputs/ # Sample input files
├── artifacts/ # Output artifacts
└── telemetry/ # Telemetry data
This project is a structured pipeline for turning raw files into tracked, structured, and usable knowledge artifacts for AI systems.