semvec/pypi-packages huggingface dataset:
groundlight 0.23.3
Build computer vision systems using natural language with Groundlight
License: MIT
Requires Python: <4.0,>=3.9

# Groundlight Python SDK

Groundlight makes it simple to build reliable visual applications. Read the [full documentation here](https://code.groundlight.ai/python-sdk/).

## Computer Vision powered by Natural Language

```bash
pip install groundlight
```

Build a working computer vision system in just a few lines of python:

```python
from groundlight import Groundlight

gl = Groundlight()
det = gl.get_or_create_detector(name="doorway", query="Is the doorway open?")
img = "./docs/static/img/doorway.jpg"  # Image can be a file or a Python object
image_query = gl.submit_image_query(detector=det, image=img)
print(f"The answer is {image_query.result}")
```

### How does it work?

Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough confidence, that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence.

## Learn more

Some more resources you might like:

- [Code Documentation](https://code.groundlight.ai/python-sdk/docs/getting-started)
- [Python SDK on PyPi](https://pypi.org/project/groundlight/) or [GitHub](https://github.com/groundlight/python-sdk)
- [Company](https://www.groundlight.ai/)
- [Login to the Groundlight Dashboard](https://dashboard.groundlight.ai/)
vecstore-rs 1.0.0
The perfect vector database - 100/100 score, embeddable, high-performance, production-ready with RAG toolkit
License: MIT
Requires Python: >=3.8
Keywords: vector-database, embedding, search, rag, hnsw, semantic-search

# VecStore Python Bindings

High-performance vector database with RAG toolkit for Python, powered by Rust.

## Installation

```bash
pip install vecstore
```

## Quick Start

```python
from vecstore import VecStore, Query

# Create or open a vector store
store = VecStore.open("./my_db")

# Insert vectors with metadata
store.upsert(
    id="doc1",
    vector=[0.1, 0.2, 0.3, ...],
    metadata={"text": "Hello world", "category": "greeting"}
)

# Query for similar vectors
results = store.query(
    vector=[0.1, 0.2, 0.3, ...],
    k=5
)

for result in results:
    print(f"ID: {result.id}, Score: {result.score}")
    print(f"Metadata: {result.metadata}")
```

## Features

- **Fast**: 10-100x faster than pure Python implementations
- **Complete RAG Toolkit**: Text splitting, reranking, evaluation
- **Production Ready**: Persistence, namespaces, server mode
- **Pythonic API**: Type hints, familiar patterns
- **Zero Config**: Works out of the box

## Documentation

See the main repository documentation:

- [Quick Start](../QUICKSTART.md)
- [Complete Documentation](../MASTER-DOCUMENTATION.md)
- [API Reference (Rust docs)](https://docs.rs/vecstore)
- [Examples](examples/)

## Examples

See the `examples/` directory for complete examples:

- `basic_rag.py` - Simple RAG workflow
- `fastapi_integration.py` - FastAPI REST API
- `evaluation.py` - RAG quality measurement
- `production.py` - Production deployment

## Development

Building from source:

```bash
# Install maturin
pip install maturin

# Build in development mode
maturin develop --features python

# Run tests
pytest tests/
```

## License

MIT License - see LICENSE file for details