https://github.com/codepawl/featcat
AI-Powered Feature Catalog for teams
https://github.com/codepawl/featcat
Last synced: 3 months ago
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AI-Powered Feature Catalog for teams
- Host: GitHub
- URL: https://github.com/codepawl/featcat
- Owner: codepawl
- License: mit
- Created: 2026-04-03T07:38:53.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-04-07T09:14:06.000Z (3 months ago)
- Last Synced: 2026-04-07T12:02:26.311Z (3 months ago)
- Language: Python
- Size: 665 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# featcat
**AI-Powered Feature Catalog for Data Science Teams**
[Tiếng Việt](docs/README-vi.md)
featcat is a lightweight Feature Catalog designed for Data Science teams. It is **not** a Feature Store (no online serving) — it's a metadata management tool with an AI layer for searching, documenting, and monitoring feature quality.
## The Problem
- **Features scattered everywhere**: Parquet files stored across local disks, S3, and MinIO — nobody knows what features exist
- **Missing documentation**: Dataset columns have no descriptions; new team members don't know what `avg_session_duration` means
- **Hard to find the right features**: Starting a new project (e.g. churn prediction) with no idea which features are already available
- **Undetected data drift**: Feature distributions change silently until model performance degrades
## Key Features
| Module | Description | Phase |
|--------|-------------|-------|
| **Catalog** | Register data sources, scan Parquet to auto-extract schema + stats | 1 |
| **AI Discovery** | Describe a use case → AI recommends relevant features + suggests new ones | 2 |
| **Auto-doc** | LLM automatically generates documentation for each feature | 2 |
| **NL Query** | Ask in natural language (English or Vietnamese), AI finds relevant features | 2 |
| **Monitoring** | PSI drift detection, null spikes, range violations | 3 |
| **TUI** | Terminal UI with dashboard, feature browser, AI chat | 3 |
| **S3 Support** | Read Parquet directly from S3/MinIO — never copies data locally | 1 |
| **Caching** | Cache LLM responses to speed up doc generation and NL queries | 3 |
## Quick Start
```bash
# 1. Clone and install
git clone https://github.com/codepawl/featcat.git && cd featcat
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
# 2. Initialize catalog
featcat init
# 3. Register and scan a data source
featcat source add device_perf /data/features/device_performance.parquet
featcat source scan device_perf
# 4. Browse features
featcat feature list
featcat feature info device_perf.cpu_usage
# 5. (Optional) Enable AI features — requires Ollama
ollama serve &
ollama pull qwen2.5:7b
featcat discover "churn prediction for telecom customers"
featcat ask "features related to user behavior"
```
## TUI (Terminal UI)
```bash
uv pip install -e ".[tui]"
featcat ui
```
Keybindings: `D` Dashboard | `F` Features | `M` Monitor | `C` Chat | `Q` Quit | `?` Help
## System Health Check
```bash
featcat doctor
```
```
[x] Python 3.10+
[x] SQLite catalog exists (catalog.db)
[x] Ollama running at localhost:11434
[x] Model qwen2.5:7b available
[x] 14 features registered
[x] 10 features have docs (71.4%)
[ ] 2 features have drift warnings
```
## Tech Stack
- **Python 3.10+** | **SQLite** (metadata only, never copies data)
- **Typer** + **Rich** (CLI) | **Textual** (TUI)
- **PyArrow** (Parquet schema + stats) | **s3fs** (S3/MinIO)
- **Ollama** (local LLM) | **Pydantic** (models + config)
## Project Structure
```
featcat/
├── catalog/ # Models, DB, scanner, storage backends
├── llm/ # LLM abstraction (Ollama, llama.cpp)
├── plugins/ # Discovery, Autodoc, Monitoring, NL Query
├── utils/ # Prompts, catalog context, statistics, cache
├── tui/ # Textual TUI (screens, widgets)
├── config.py # Pydantic settings
└── cli.py # Typer CLI entry point
```
## License
MIT