Metadata-Version: 2.4
Name: orchid-rag-chroma
Version: 1.0.2
Summary: ChromaDB vector-backend plugin for the Orchid AI framework
Project-URL: Homepage, https://github.com/gadz82/orchid-rag-chroma
Project-URL: Issues, https://github.com/gadz82/orchid-rag-chroma/issues
License: MIT
License-File: LICENSE
Keywords: agents,ai,chromadb,rag,vector
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Requires-Dist: chromadb>=0.5.0
Requires-Dist: orchid-ai>=1.8.2
Provides-Extra: dev
Requires-Dist: pytest-asyncio>=0.25.0; extra == 'dev'
Requires-Dist: pytest-cov>=6.0; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.9.0; extra == 'dev'
Description-Content-Type: text/markdown

# orchid-rag-chroma

ChromaDB vector backend plugin for the [Orchid AI](https://github.com/gadz82/orchid) framework.

## What it provides

- `ChromaRepository` — implements `OrchidVectorStoreRepository` backed by ChromaDB

## Installation

```bash
pip install orchid-rag-chroma
```

## Usage

Reference `vector_backend: chroma` in your `agents.yaml`:

```yaml
rag:
  vector_backend: chroma
  chroma_client_type: http
  chroma_host: localhost
  chroma_port: 8000
```

Or build it programmatically:

```python
from orchid_rag_chroma import ChromaRepository
from langchain_community.embeddings import OllamaEmbeddings

repo = ChromaRepository(
    client_type="http",
    host="localhost",
    port=8000,
    embeddings=OllamaEmbeddings(model="nomic-embed-text"),
    embedding_dimension=768,
)
```

## Development

```bash
cd orchid-rag-chroma
pip install -e ".[dev]"
pytest tests/ -x
ruff check orchid_rag_chroma/
```

## License

MIT
