semvec/pypi-packages huggingface dataset:
boxabar 0.1.0
Turli xil xabar yuborish usullarini qo'llab-quvvatlovchi Python kutubxonasi
Requires Python: >=3.6

Boxabar - bu turli xil xabar yuborish usullarini (SMS, Email, Telegram) qo'llab-quvvatlovchi Python kutubxonasi.

## O'rnatish

```bash
pip install boxabar
```

## Foydalanish usuli

### SMS xabar yuborish

```python
from boxabar import SMSMessenger, Message

# Messenger yaratish
sms = SMSMessenger(config={
    'default_sender': '+998901234567'
})

# Xabar yuborish
xabar = Message(
    text="Salom, bu test xabar!",
    sender="+998901234567",
    recipient="+998901234568"
)
sms.send(xabar)

# Yoki qisqaroq usul
sms.send("Bu ham test xabar")
```

### Email xabar yuborish

```python
from boxabar import EmailMessenger

email = EmailMessenger(config={
    'default_sender': 'noreply@example.com',
    'default_subject': 'Muhim xabar'
})

email.send("Bu email xabar matni")
```

### Telegram xabar yuborish

```python
from boxabar import TelegramMessenger

telegram = TelegramMessenger(config={
    'bot_token': '12345:ABCdefGHIjklMNOpqrSTUvwxYZ',
    'bot_username': 'BoxabarTestBot',
    'chat_id': '123456789'
})

telegram.send("Telegram orqali yuborilgan xabar")
```
pyojure 0.2.1
Make Python More Like Clojure

# pyojure

make python more like clojure

![pyojure is pure joy](6vc5do.jpeg "python doesn't have to be so hard")

## Usage

```pycon
>>> from pyojure import *
>>> mapv(print, dir())
```

```pycon
>>> assoc({"this": "is"}, "very", "cool")
{'this': 'is', 'very': 'cool'}
>>> comp(str, inc, int)('123')
'124'
>>> list(partition(2, [1,2,3,4,5,6,7,8,9]))
[[1, 2], [3, 4], [5, 6], [7, 8], [9]]
```

### Doc

```pycon
>>> import pyojure
>>> help(pyojure.core)
```

## Caveats
* Clojure has much more liberal syntax for identifiers.  Standard naming transpositions are attempted.
  * `-` -> `_` e.g. `every-pred` -> `every_pred`
  * `?` predicate suffix -> `is_`/`has_`/etc. predicate prefix e.g., `odd?` -> `is_odd`
* Some Clojure function names indicate types that are known by different names in Python.
  * E.g., the `v` in `mapv` refers to the Clojure `vector` type, which is equivalent to a Python `list`
  * Both names are provided _for now_ i.e., `mapv` == `mapl`
uhsr-retrieval 0.1.3
Unified Hyperbolic Spectral Retrieval (UHSR) - a novel text retrieval algorithm combining lexical and semantic search.
Requires Python: >=3.6
Keywords: uhsr, text retrieval, BM25, FAISS, semantic search, lexical search, spectral re-ranking, machine learning, NLP

# Unified Hyperbolic Spectral Retrieval (UHSR)

Unified Hyperbolic Spectral Retrieval (UHSR) is a novel text retrieval algorithm that fuses lexical search (using BM25) with semantic search (using dense embeddings) into a unified, robust, and scalable system. It leverages advanced techniques such as logistic normalization, harmonic fusion, and spectral re-ranking based on graph Laplacian analysis to produce interpretable relevance scores within the [0,1] range.

## Key Features

- **Hybrid Retrieval:** Combines BM25 for lexical scoring and dense vector semantic similarity for contextual understanding.
- **Advanced Fusion:** Uses logistic normalization and harmonic fusion to integrate multiple scoring signals.
- **Spectral Re-Ranking:** Employs spectral analysis (using the graph Laplacian and Fiedler vector) to boost central, highly relevant candidates.
- **Metric Flexibility:** Supports multiple semantic similarity metrics (cosine, euclidean, Mahalanobis) to suit various datasets.
- **Interpretable Scores:** Final relevance scores are normalized to the [0,1] range.
- **Scalable:** Designed to work with both small and large datasets using FAISS for fast approximate nearest neighbor search.

## Overview

UHSR provides an end-to-end text retrieval pipeline that starts with raw documents and ends with a ranked list of documents. It first applies BM25 to perform fast lexical filtering, then computes semantic similarity using dense embeddings. The two scoring components are fused via a harmonic mean after logistic normalization, ensuring that both lexical and semantic aspects contribute effectively. Finally, a spectral re-ranking step based on graph Laplacian analysis refines the ranking by boosting documents that are centrally located among the top candidates.

## Intended Use

UHSR is intended for research and educational purposes and can serve as a strong foundation for further development in text retrieval and natural language processing applications.

For more details, visit the [GitHub repository](https://github.com/vedaant00/uhsr-retrieval).