semvec/pypi-packages huggingface dataset:
brollm 0.1.2
When you think about light weight LLMs, call brollm!
Requires Python: >=3.11

# brollm

A lightweight Python library providing unified interfaces for LLM models, embeddings, and rerankers. Built for AI agent development with consistent APIs across different providers.

## Features

- **Unified Interface**: Same API across AWS Bedrock and Ollama
- **LLM Models**: Chat completion with system prompts and message history
- **Embeddings**: Text embedding generation for semantic search
- **Rerankers**: Document reranking capabilities (base class ready)
- **Lightweight**: Minimal dependencies, maximum flexibility

## Installation

```bash
pip install brollm
```

or

```bash
uv add brollm
```

## Quick Start

### AWS Bedrock

```python
from brollm import BedrockChat, BedrockEmbedding

# Chat completion
chat = BedrockChat(model_name="us.meta.llama3-2-11b-instruct-v1:0")
messages = [
    chat.UserMessage("What is machine learning?"),
    chat.AIMessage("Machine learning is..."),
    chat.UserMessage("Give me an example")
]
response = chat.run("You are a helpful AI assistant", messages)
print(response)

# Embeddings
embedding = BedrockEmbedding()
vector = embedding.embed_text("Hello world")
print(f"Embedding dimension: {len(vector)}")
```

### Ollama

```python
from brollm import OllamaChat, OllamaEmbedding

# Chat completion
chat = OllamaChat(model_name="qwen3:8b")
messages = [
    chat.UserMessage("Explain quantum computing"),
    chat.AIMessage("Quantum computing uses..."),
    chat.UserMessage("What are the applications?")
]
response = chat.run("You are a physics expert", messages)
print(response)

# Embeddings
embedding = OllamaEmbedding(model_name="nomic-embed-text")
vectors = embedding.embed_texts(["Hello", "World", "AI"])
print(f"Generated {len(vectors)} embeddings")
```

### Multimodal Support (Bedrock)

```python
from brollm import BedrockChat

chat = BedrockChat()
with open("image.jpg", "rb") as f:
    image_bytes = f.read()

messages = [
    chat.UserMessage("Describe this image", image_bytes=image_bytes, image_format="jpeg")
]
response = chat.run("You are a vision AI assistant", messages)
print(response)
```

## Provider Switching

Switch between providers without changing your code structure:

```python
# Use Bedrock
llm = BedrockChat(temperature=0.7)

# Switch to Ollama
llm = OllamaChat(temperature=0.7)

# Same interface for both
messages = [llm.UserMessage("Hello AI!")]
response = llm.run("You are helpful", messages)
```

## Configuration

### Bedrock
```python
chat = BedrockChat(
    model_name="us.meta.llama3-2-11b-instruct-v1:0",
    temperature=0.7,
    region_name="us-west-2",
    aws_access_key_id="your-key",  # Optional, uses default AWS config
    aws_secret_access_key="your-secret",  # Optional
    aws_session_token="your-token"  # Optional
)
```

### Ollama
```python
chat = OllamaChat(
    model_name="qwen3:8b",
    temperature=0.7,
    base_url="http://localhost:11434"  # Default Ollama endpoint
)
```

## License

MIT License
pyinr 0.0.5
A simple package to convert Indian Rupees to other currencies.
Requires Python: >=3.7

# pyinr

A simple Python package to convert currencies.

## Installation

You can install `pyinr` directly from PyPI using pip:

```bash
pip install pyinr
```

## Usage

Here's how you can use the `pyinr` module to perform currency conversions:

```python
from pyinr import convert

# Example: Convert 100 USD to EUR
eur_amount = convert(100, "USD", "EUR")
print(f"100 USD is equal to {eur_amount} EUR")

# Example: Convert 500 JPY to INR
inr_amount = convert(500, "JPY", "INR")
print(f"500 JPY is equal to {inr_amount} INR")

# Example: Convert 250 INR to USD
usd_amount = convert(250, "INR", "USD")
print(f"250 INR is equal to {usd_amount} USD")
```

**Note:** The `convert` function fetches live exchange rates from `exchangerate-api.com`. For real-world applications, consider API rate limits and error handling.

## PyPI Instructions

This package is available on PyPI. You can find it by searching for `pyinr` on the [PyPI website](https://pypi.org/).

To install it, simply run:

```bash
pip install pyinr
```

If you encounter any issues or have suggestions, please report them on the [Bug Tracker](https://github.com/ganeshdatta23/pyinr/issues).
hoy 1.0.6
A dead simple notifier.
Requires Python: >=3.9

Hoy!  [![PyPI - Version](https://img.shields.io/pypi/v/hoy?style=flat-square)](https://pypi.org/project/hoy/)
====

A dead simple notifier.

Be informed when your scripts finish running!


Installation
------------

```bash
pipx install hoy
# or
uv tool install hoy
```


Usage
-----

Simply run `hoy` after a long-running process (example: `sleep 5`):

```bash
sleep 5; hoy
# Or if you want a custom message:
sleep 5; hoy "All clear, my dear!" # it'll also work without quotes
# Or if you want different messages for success and failure:
sleep 5; hoy $status # or `hoy $?` depending on your shell
# Or if you want a custom message again:
sleep 5 && hoy Success! || hoy Fail!
```
msuaccalc 0.1.0
Decode Windows userAccountControl flags.
Requires Python: >=3.9

# MSuacCalc

`msuaccalc` is a lightweight Python module to decode `userAccountControl` flags used in Active Directory (AD) user objects.

## Installation

```bash
pip install msuaccalc
```

Or with Poetry:

```bash
poetry add msuaccalc
```

## Usage

### As a Python module

```python
import msuaccalc

# Decode UAC value
print(msuaccalc(66048))
# Output: ['NORMAL_ACCOUNT', 'DONT_EXPIRE_PASSWORD']
```

### CLI (after installation)

```bash
msuaccalc 66048
# Output: ['NORMAL_ACCOUNT', 'DONT_EXPIRE_PASSWORD']
```

## Flags Reference

Based on [Microsoft documentation](https://learn.microsoft.com/en-us/troubleshoot/windows-server/active-directory/useraccountcontrol-manipulate-account-properties#list-of-property-flags)