| Name | minivnn JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | |
| Summary | Exact nearest neighbor search library for those times when "approximate" just won't cut it (or is simply overkill) |
| upload_time | 2023-04-28 16:54:54 |
| maintainer | |
| docs_url | None |
| author | aismlv |
| requires_python | >=3.9,<4.0 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# minivnn
[](https://codecov.io/gh/aismlv/minivnn)
[](https://pypi.org/project/minivnn/)
[](https://opensource.org/licenses/Apache-2.0)
`minivnn` (pronounced "minivan" 🚐) is an exact nearest neighbor search Python library for those times when "approximate" just won't cut it (or is simply overkill).
## Installation
Install `minivnn` using `pip`:
```bash
pip install minivnn
```
## Usage
Here's an example of how to use minivnn:
```python
from minivnn import Index
import numpy as np
# Create an index with 128-dimensional embeddings and cosine similarity metric
index = Index(dim=128, metric="cosine")
# Add embeddings to the index
embedding1 = np.random.rand(128)
embedding2 = np.random.rand(128)
embedding3 = np.random.rand(128)
index.add([1, 2, 3], [embedding1, embedding2, embedding3])
# Delete embeddings from the index
index.delete_items([3])
# Query the index for the nearest neighbor of a given embedding
query_embedding = np.random.rand(128)
result = index.query(query_embedding, k=1)
print(result) # Returns [(index, similarity)] of the nearest neighbor
```
Supported metrics are cosine similarity and dot product.
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