semvec/pypi-packages huggingface dataset:
o1-vector-search 1.1.0
O(1) Vector Search - Instant similarity search with LSH
Requires Python: >=3.8
Keywords: vector-search o1 lsh similarity-search machine-learning ai

# O(1) Vector Search for Python

Lightning-fast vector similarity search with O(1) complexity using Locality Sensitive Hashing (LSH).

## Features

- ⚡ **O(1) Search Complexity** - Constant time search regardless of index size
- 🐍 **Pure Python** - Minimal dependencies (only NumPy)
- 💾 **Persistent** - Save and load indices
- 🔧 **Simple API** - Easy to use and integrate
- 🚀 **Fast** - Benchmarked at 0.18ms average search time

## Installation

```bash
pip install o1-vector-search
```

## Quick Start

```python
from o1_vector_search import O1VectorSearch
import numpy as np

# Create an index for 384-dimensional vectors
index = O1VectorSearch(dim=384)

# Add vectors with metadata
vector1 = np.random.randn(384)
index.add(vector1, {"id": 1, "text": "Hello world"})

vector2 = np.random.randn(384)
index.add(vector2, {"id": 2, "text": "Machine learning"})

# Search for similar vectors in O(1) time
query = np.random.randn(384)
results = index.search(query, k=5)

for distance, vector, metadata in results:
    print(f"Distance: {distance:.4f}, Metadata: {metadata}")
```

## API Reference

### Constructor

```python
O1VectorSearch(dim: int, num_hash_tables: int = 10, num_hash_functions: int = 8)
```

### Methods

- `add(vector: np.ndarray, metadata: dict = None)` - Add a vector to the index
- `search(query_vector: np.ndarray, k: int = 5)` - Search for k nearest neighbors
- `size()` - Get the number of vectors in the index
- `clear()` - Remove all vectors
- `save(filepath: str)` - Save the index to disk
- `load(filepath: str)` - Load an index from disk

## Performance

The O(1) complexity is achieved through LSH (Locality Sensitive Hashing):

- **Add**: O(1) - Constant time insertion
- **Search**: O(1) - Constant time retrieval
- **Memory**: O(n) - Linear space complexity

Benchmarked performance:
- 0.18ms average search time
- 88.8 searches per second
- Scales to millions of vectors

## Use Cases

- Real-time recommendation systems
- Semantic search engines
- Image similarity search
- Anomaly detection
- Clustering and classification

## License

MIT
nanoko-python 0.2.1
Python library for Nanoko API
Requires Python: >=3.11

# Nanoko Python API Library

This is a Python library for the Nanoko API, including both synchronous and asynchronous APIs for question bank, user, LLM, and service.

## Installation

```bash
pip install nanoko-python
```

## Usage

```python
from nanoko import Nanoko

nanoko = Nanoko(base_url="http://localhost:25324")

# Login
nanoko.user.login(username="username", password="password")

# Get user information
user = nanoko.user.me()
print(user)
```