Metadata-Version: 2.4
Name: agentg2v
Version: 0.1.0
Summary: AI-assisted pipeline that converts RDF Turtle (.ttl) knowledge graphs into a ChromaDB vector database
Project-URL: Homepage, https://github.com/Shyam-Sundar-Reddy/agentg2v
Project-URL: Repository, https://github.com/Shyam-Sundar-Reddy/agentg2v
License: MIT
License-File: LICENSE
Requires-Python: >=3.12
Requires-Dist: rich>=13.0
Requires-Dist: typer>=0.12
Provides-Extra: dev
Requires-Dist: pytest>=8; extra == 'dev'
Description-Content-Type: text/markdown

# agentg2v

An AI-assisted pipeline that converts RDF Turtle (.ttl) knowledge graphs
into a ChromaDB vector database — for AI applications such as RAG and
semantic search.

**Status: early / pre-alpha.** This is the initial project scaffold; the
graph-to-vector pipeline described in [goal.md](./goal.md) hasn't been
built yet.

## Why

Organizations have knowledge stored in TTL/RDF format, but modern AI
applications need vector databases. Traditional graph data migration moves
the data but loses the semantic meaning — entity information, relationship
context, and search capability. `agentg2v` aims to transform a TTL
knowledge graph into an AI-ready vector database while preserving all of
that.

## Install

```bash
uv add agentg2v
pip install agentg2v
```

## Usage

```bash
agentg2v about
agentg2v --version
```

## Development

This project uses [uv](https://docs.astral.sh/uv/) for dependency management.

```bash
uv sync --extra dev    # install package + dev deps into .venv
uv run pytest -q       # run tests
uv run agentg2v about  # run the CLI
```
