semvec/pypi-packages huggingface dataset:
used_addr_check 0.2.0
A tool to efficiently check if a Bitcoin Address has ever been used before
Requires Python: >=3.10

# used_addr_check (Python)
A tool to efficiently check if a Bitcoin Address has ever been used before

## Description

Based on [loyce.club's "all bitcoin addresses ever used" list](http://alladdresses.loyce.club/all_Bitcoin_addresses_ever_used_sorted.txt.gz), this library and CLI tool can search the list very, very fast and efficiently.

## Features

* Lookup either a single address, or a long list of potential addresses.
* CLI and library options
* Generates a binary search tree index from the text file, and then uses that to search for the address.

## Getting Started

This project depends on the large address list being downloaded and extracted.

```bash
# Download the zipped text file of used addresses, where each line is a used Bitcoin address.
wget http://alladdresses.loyce.club/all_Bitcoin_addresses_ever_used_sorted.txt.gz

# Extract the file
gunzip -d ./all_Bitcoin_addresses_ever_used_sorted.txt.gz --stdout | pv > all_Bitcoin_addresses_ever_used_sorted.txt
```

Optionally, if you intend to use the `scan_file` subcommand, install `ripgrep`
for optimal performance:

```bash
# First, install cargo/rust.
# Then, run:
cargo install ripgrep
```


## Usage - CLI

```bash
uv tool install used_addr_check

# Download and extract the required file:
wget http://alladdresses.loyce.club/all_Bitcoin_addresses_ever_used_sorted.txt.gz
gunzip -d ./all_Bitcoin_addresses_ever_used_sorted.txt.gz --stdout | pv > addr_list.txt

# Generate the index file (optional):
used_addr_check index -f ./addr_list.txt
# the index file is now at: ./addr_list.index.parquet

# Search a couple of addresses:
used_addr_check search -f ./addr_list.txt -s moW9o415jNfgyuzytEMZD84Kovri5DJ64e -s mncqTEYTidNdbqGZnXTd1JFYRrruuh5StV

# Search for a long list of addresses (extracted by regex):
used_addr_check scan_file -f ./addr_list.txt -n file_with_addresses_to_lookup.txt
```

## Usage - Library

This example will generate the index file, if required.

```python
from used_addr_check import search_multiple_in_file

needles = [
    'moW9o415jNfgyuzytEMZD84Kovri5DJ64e',
    'mncqTEYTidNdbqGZnXTd1JFYRrruuh5StV'
]
haystack_file_path = './addr_list.txt'

addresses_found_list: list[str] = search_multiple_in_file(
    haystack_file_path=haystack_file_path,
    needles=needled,
)
print(f"{addresses_found_list=}")
```

## Performance Notes

* With the default indexing size of one index entry per 1000 addresses in the "haystack" file, the index is a 140MB Parquet file.
* On a 2023 mid-range laptop with an SSD:
    * Indexing takes 4 minutes.
    * Addresses can be searched at 20 query addresses ("needles") per sec.

There are certainly opportunities for further improvement, but this performance is adequate.

## Contributing

Please Star this repo if it's helpful.

Please open GitHub Issues and Pull Requests with features/bugs/fixes.

## Future Features

* Optionally disable `loguru` logging in subfunctions
* Convert "found address" result to an iterator.
* Test cases.
voicegain-speech 1.130.0
Voicegain Speech-to-Text Python SDK

# Voicegain Speech-to-Text Python SDK

Python SDK for the [Voicegain](https://www.voicegain.ai) [Speech-to-Text API](https://portal.voicegain.ai/api/v1/index.html). 

This API allows for large vocabulary speech-to-text transcription as well as grammar-based speech recognition. Both real-time and offline use cases are supported.

You can see the core Voicegain API documentation [here](https://portal.voicegain.ai/api/v1/index.html). 

The complete documentation for the API covered by this SDK is available [here](https://portal.voicegain.ai/api-documentation) - this link requires an account on the Voicegain portal - see below for how to sign up. 

## Requirements

In order to use this API you need account with Voicegain. You can create an account by signing up on [Voicegain Portal](https://portal.voicegain.ai/signup). No credit card required to sign up.

You can see pricing [here](https://www.voicegain.ai/pricing) - basically, it is 1 cent a minute for off-line and 1.25 cents a minute for real-time. There is a Free Tier of 600 minutes that renews each month.

## Installation

From [PyPI](https://pypi.org/project/voicegain-speech/) directly:

```
pip install voicegain_speech
```

## Examples

* sync_transcribe example:

configuration:

```python
from voicegain_speech import ApiClient
from voicegain_speech import Configuration
from voicegain_speech import TranscribeApi
import base64


# configure your JWT token
JWT = "Your <JWT>"

configuration = Configuration()
configuration.access_token = JWT

api_client = ApiClient(configuration=configuration)
```
transcribe local file:
```python
transcribe_api = TranscribeApi(api_client)
file_path = "Your local file path"

with open(file_path, "rb") as f:
    audio_base64 = base64.b64encode(f.read()).decode()

response = transcribe_api.asr_transcribe_post(
    sync_transcription_request={
        "audio": {
            "source": {
                "inline": {
                    "data": audio_base64
                }
            }
        }
    }
)

alternatives = response.result.alternatives
if alternatives:
    local_result = alternatives[0].utterance
    print("result from file: ", local_result)

else:
    local_result = None
    print("no transcription")

```

More examples can be found in [examples](https://github.com/voicegain/python-sdk/tree/master/examples) folder on our [GitHub](https://github.com/voicegain/python-sdk)

---
Learn more about the Voicegain Voice AI Platform at [www.voicegain.ai](https://www.voicegain.ai)
rotavision-gati 0.0.1
Rotavision Gati - Fleet & Mobility Intelligence
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, rotavision, trust

# rotavision-gati

Rotavision Gati - Fleet & Mobility Intelligence

## Installation

```bash
pip install rotavision
```

> **Note:** This is a namespace placeholder. Please install the main `rotavision` package.

## Documentation

Visit [rotavision.com/docs](https://rotavision.com/docs) for full documentation.

## License

Apache 2.0
rotavision-orchestrate 0.0.1
Rotavision Orchestrate - Multi-Agent Platform
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, rotavision, trust

# rotavision-orchestrate

Rotavision Orchestrate - Multi-Agent Platform

## Installation

```bash
pip install rotavision
```

> **Note:** This is a namespace placeholder. Please install the main `rotavision` package.

## Documentation

Visit [rotavision.com/docs](https://rotavision.com/docs) for full documentation.

## License

Apache 2.0
rotavision-sankalp 0.0.1
Rotavision Sankalp - Sovereign AI Gateway
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, rotavision, trust

# rotavision-sankalp

Rotavision Sankalp - Sovereign AI Gateway

## Installation

```bash
pip install rotavision
```

> **Note:** This is a namespace placeholder. Please install the main `rotavision` package.

## Documentation

Visit [rotavision.com/docs](https://rotavision.com/docs) for full documentation.

## License

Apache 2.0
rotavision-dastavez 0.0.1
Rotavision Dastavez - Document AI + Browser Agents
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, rotavision, trust

# rotavision-dastavez

Rotavision Dastavez - Document AI + Browser Agents

## Installation

```bash
pip install rotavision
```

> **Note:** This is a namespace placeholder. Please install the main `rotavision` package.

## Documentation

Visit [rotavision.com/docs](https://rotavision.com/docs) for full documentation.

## License

Apache 2.0
rotavision-guardian 0.0.1
Rotavision Guardian - AI Reliability Monitoring
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, rotavision, trust

# rotavision-guardian

Rotavision Guardian - AI Reliability Monitoring

## Installation

```bash
pip install rotavision
```

> **Note:** This is a namespace placeholder. Please install the main `rotavision` package.

## Documentation

Visit [rotavision.com/docs](https://rotavision.com/docs) for full documentation.

## License

Apache 2.0
rotavision-vishwas 0.0.1
Rotavision Vishwas - Trust, Fairness & Explainability
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, rotavision, trust

# rotavision-vishwas

Rotavision Vishwas - Trust, Fairness & Explainability

## Installation

```bash
pip install rotavision
```

> **Note:** This is a namespace placeholder. Please install the main `rotavision` package.

## Documentation

Visit [rotavision.com/docs](https://rotavision.com/docs) for full documentation.

## License

Apache 2.0
rotavision-sdk 0.0.1
Official Python SDK for Rotavision AI Trust Infrastructure
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, rotavision, trust

# rotavision-sdk

Official Python SDK for Rotavision AI Trust Infrastructure

## Installation

```bash
pip install rotavision
```

> **Note:** This is a namespace placeholder. Please install the main `rotavision` package.

## Documentation

Visit [rotavision.com/docs](https://rotavision.com/docs) for full documentation.

## License

Apache 2.0
rotavision-ai 0.0.1
Official Python SDK for Rotavision AI Trust Infrastructure
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, rotavision, trust

# rotavision-ai

Official Python SDK for Rotavision AI Trust Infrastructure

## Installation

```bash
pip install rotavision
```

> **Note:** This is a namespace placeholder. Please install the main `rotavision` package.

## Documentation

Visit [rotavision.com/docs](https://rotavision.com/docs) for full documentation.

## License

Apache 2.0
rotavision 0.1.0
Official Python SDK for Rotavision AI Trust Infrastructure
Requires Python: >=3.8
Keywords: ai, fairness, governance, india, llm, monitoring, rotavision, trust

# Rotavision Python SDK

Official Python client for [Rotavision](https://rotavision.com) AI Trust Infrastructure.

## Installation

```bash
pip install rotavision
```

## Quick Start

```python
from rotavision import Rotavision

client = Rotavision(api_key="rv_...")

# Analyze for bias (Vishwas)
result = client.vishwas.analyze(
    text="Loan application response...",
    demographics=["caste", "gender", "region"]
)
print(result.bias_score)

# Monitor AI output (Guardian)
result = client.guardian.monitor(
    prompt="What is the RBI repo rate?",
    response="The RBI repo rate is 6.5%...",
    checks=["hallucination", "factuality"]
)
print(result.status)

# Extract from documents (Dastavez)
result = client.dastavez.extract(
    document=document_bytes,
    document_type="aadhaar",
    mask_pii=True
)
print(result.fields)
```

## Products

| Product | Description |
|---------|-------------|
| **Vishwas** | Trust, Fairness & Explainability |
| **Guardian** | AI Reliability Monitoring |
| **Dastavez** | Document AI + Browser Agents |
| **Sankalp** | Sovereign AI Gateway |
| **Orchestrate** | Multi-Agent Platform |
| **Gati** | Fleet & Mobility Intelligence |

## Documentation

- [API Reference](https://rotavision.com/docs/api/)
- [SDK Documentation](https://rotavision.com/docs/sdks/)
- [Integration Guides](https://rotavision.com/integrations/)

## Requirements

- Python 3.8+
- API key from [Rotavision](https://rotavision.com/contact/)

## License

Apache 2.0 - See [LICENSE](LICENSE) for details.
hw-compat-matrix 0.3.0
Canonical CUDA/GPU compatibility metadata for hardware compatibility checking
Requires Python: >=3.9
Keywords: compatibility, cuda, driver, gpu, hardware, nvidia

# hw-compat-matrix

Canonical CUDA/GPU compatibility metadata for hardware compatibility checking.

## Overview

This package provides structured data and utilities for:
- CUDA toolkit version to minimum driver version mapping
- NVIDIA SM (Streaming Multiprocessor) version to GPU architecture mapping
- GPU name normalization across cloud providers (Vast.ai, AWS, Lambda, RunPod, Azure)

## Installation

```bash
pip install hw-compat-matrix
```

## Usage

```python
from hw_compat_matrix import (
    get_min_driver_for_cuda,
    get_architecture_for_sm,
    normalize_gpu_name,
)

# Get minimum driver version for a CUDA toolkit version
min_driver = get_min_driver_for_cuda("12.8")  # Returns "570.26"

# Get GPU architecture for an SM version
arch = get_architecture_for_sm("89")  # Returns "Ada Lovelace"

# Normalize GPU names from different providers
canonical = normalize_gpu_name("RTX_4090", provider="vastai")  # Returns "RTX 4090"
```

### Loading Raw Data

```python
from hw_compat_matrix import (
    load_cuda_driver_matrix,
    load_nvidia_sm_map,
    load_gpu_aliases,
)

# Load raw data as Pydantic models
cuda_matrix = load_cuda_driver_matrix()
sm_map = load_nvidia_sm_map()
gpu_aliases = load_gpu_aliases()
```

## Data Sources

- **cuda_driver_matrix.yaml**: From [CUDA Toolkit Release Notes](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/)
- **nvidia_sm_map.yaml**: From [CUDA GPUs](https://developer.nvidia.com/cuda-gpus)
- **gpu_name_aliases.yaml**: Collected from provider APIs

## Contributing

### Adding New CUDA Versions

1. Update `src/hw_compat_matrix/data/cuda_driver_matrix.yaml`
2. Run validation: `pytest tests/`
3. Submit a PR

### Adding New GPU Aliases

1. Update `src/hw_compat_matrix/data/gpu_name_aliases.yaml`
2. Run validation: `pytest tests/`
3. Submit a PR

## License

MIT License - see [LICENSE](LICENSE) for details.