semvec/pypi-packages huggingface dataset:
example-package-couragesteak 0.0.1
A small example package
Requires Python: >=3.7
Keywords: python,menu,dumb_menu,windows,mac,linux

# pypi项目规范案例



<div style="text-align:center;">
    <p>
        <sub>
            Built with ❤︎ by
            <a href="https://www.couragesteak.com/">
                有勇气的牛排
            </a>
        </sub>
    </p>
</div>



## 1 安装

```shell
pip3.8 uninstall couragesteak-python-sdk-demo

pip3.8 install couragesteak-python-sdk-demo==0.0.1
pip3.8 install couragesteak-python-sdk-demo==0.0.2
```



## 2 测试、编译和上传

### 2.1 本地测试

```shell
python setup.py develop
```



编译tar.gz：

```shell
python setup.py sdist
```





编译whl：

```
pip install wheel
python setup.py bdist_wheel
```



检查

```shell
python setup.py check 
```



上传到pypi：

```shell
pip install twine
twine upload dist/*
```



## 更新日志

`0.0.1` 基础版测试
hyperdb-python 0.1.4
A hyper-fast local vector database for use with LLM Agents.
Requires Python: >=3.8

# HyperDB
<div>
<img src="https://github.com/jdagdelen/hyperDB/blob/main/_static/logo.png?raw=true" width="400" alt="HyperDB Logo">
</div>

A hyper-fast local vector database for use with LLM Agents. 

Want to invest? [Now accepting SAFEs ($135M cap minimum.)](https://www.youtube.com/watch?v=QH2-TGUlwu4)

Join the [HyperDBiscord](https://discord.gg/8YQzexAA)

## Advantages
* Simple interface compatible with _all_ large language model agents. 
* Highly optimized C++ backend vector store with HW accelerated operations via MKL BLAS. 
* Enables users to index documents with advanced features such as _ids_ and _metadata_.

## Installation

Install the package from PyPI:

```bash
pip install hyperdb-python
```

## Usage

Here's an example of using HyperDB to store and query documents with information about all 151 original pokemon _in an instant_:

```python
import json
from hyperdb import HyperDB

# Load documents from the JSONL file
documents = []

with open("demo/pokemon.jsonl", "r") as f:
    for line in f:
        documents.append(json.loads(line))

# Instantiate HyperDB with the list of documents
db = HyperDB(documents, key="info.description")

# Save the HyperDB instance to a file
db.save("demo/pokemon_hyperdb.pickle.gz")

# Load the HyperDB instance from the save file
db.load("demo/pokemon_hyperdb.pickle.gz")

# Query the HyperDB instance with a text input
results = db.query("Likes to sleep.", top_k=5)
```

Returns 
```
Name: Snorlax
Pokedex ID: 143
HP: 160
Type: normal
Weakness: fighting
Description: Very lazy. Just eats and sleeps. As its rotund bulk builds, it becomes steadily more slothful.

Name: Drowzee
Pokedex ID: 96
HP: 230
Type: psychic
Weakness: dark
Description: Puts enemies to sleep then eats their dreams. Occasionally gets sick from eating bad dreams.

Name: Pinsir
Pokedex ID: 127
HP: 160
Type: bug
Weakness: fire
Description: When the temperature drops at night, it sleeps on treetops or among roots where it is well hidden.

Name: Abra
Pokedex ID: 63
HP: 160
Type: psychic
Weakness: dark
Description: Sleeps 18 hours a day. If it senses danger, it will teleport itself to safety even as it sleeps.

Name: Venonat
Pokedex ID: 48
HP: 160
Type: bug
Weakness: fire
Description: Lives in the shadows of tall trees where it eats insects. It is attracted by light at night.
```

<img width="600" src="https://raw.githubusercontent.com/jdagdelen/hyperDB/main/_static/0B147C7D-BEB0-4E61-9397-64A460C8CE22.png"/>

*Benchmark Credit: Benim Kıçım