semvec/pypi-packages huggingface dataset:
far-search-autogpt 1.0.0
AutoGPT plugin for Federal Acquisition Regulations (FAR) search
Requires Python: >=3.10
Keywords: autogpt, far, federal-acquisition-regulations, government-contracting, plugin, ai-agent, compliance

# FAR Search AutoGPT Plugin

[![PyPI version](https://badge.fury.io/py/far-search-autogpt.svg)](https://pypi.org/project/far-search-autogpt/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**AutoGPT plugin** for searching Federal Acquisition Regulations (FAR).

Enable your AutoGPT agent to search government contracting regulations, verify compliance requirements, and cite accurate FAR clauses.

## Installation

```bash
pip install far-search-autogpt
```

## Setup

### Option 1: Auto-Registration (Recommended)

Just install and use - the plugin auto-registers on first search:

```bash
pip install far-search-autogpt
# Add to your AutoGPT plugins
# First search will auto-register and display your API key
```

### Option 2: With API Key

```bash
# Get an API key
curl -X POST https://far-rag-api-production.up.railway.app/v1/register \
  -H "Content-Type: application/json" \
  -d '{"agent_id": "my-autogpt-agent"}'

# Set environment variable
export FAR_API_KEY=far_live_...

# Install and use
pip install far-search-autogpt
```

## Usage

The plugin provides these commands to your AutoGPT agent:

### Search FAR

```python
plugin = FARSearchPlugin()

# Search for relevant regulations
result = plugin.search_far("cybersecurity requirements for DoD contracts")
print(result)
```

### Get Specific Clause

```python
# Get a specific clause by ID
result = plugin.get_far_clause("52.204-2")
print(result)
```

## AutoGPT Integration

Add to your AutoGPT configuration:

```yaml
# .env or config
ALLOWLISTED_PLUGINS=far-search-autogpt
```

Then your agent can use commands like:
- "Search FAR for small business requirements"
- "Get FAR clause 52.203-1"
- "Verify compliance requirements for cybersecurity"

## Use Cases

- **Proposal Writing**: Find relevant FAR clauses for government proposals
- **Compliance Verification**: Verify AI-generated FAR citations
- **Contract Analysis**: Research regulatory requirements
- **Training**: Learn about federal acquisition rules

## Pricing

| Plan | Price | Queries/Month |
|------|-------|---------------|
| **Free** | $0 | 500 |
| **Pro** | $29/mo | 5,000 |
| **Ultra** | $199/mo | 150,000 |

Auto-registers on first use. Upgrade at [RapidAPI](https://rapidapi.com/yschang/api/far-rag-federal-acquisition-regulation-search).

## Related Packages

| Package | Use Case |
|---------|----------|
| **far-search** | Lightweight core SDK |
| **far-search-tool** | LangChain tool |
| **far-search-autogpt** (this) | AutoGPT plugin |
| **far-search-crewai** | CrewAI multi-agent tool |
| **far-oracle** | MCP Server for Claude Desktop |

## License

MIT License - see [LICENSE](LICENSE) for details.
easypv 3.9.0
EasyPV is a Python tool designed for statistical analysis.
Requires Python: >=3.8

# EasyPV

EasyPV is a Python tool designed for statistical analysis. It facilitates the organization and analysis of monitoring data exported from the National Medical Device Adverse Event Information System, the National Adverse Drug Reaction Monitoring System, and the National Cosmetic Adverse Reaction Monitoring System. EasyPV provides functionalities such as data import, data viewing, basic statistical analysis, signal detection, and report generation.

## Features

Data Import and Export: Supports multiple data formats for import and export.
Basic Statistical Analysis: Includes statistics by city/county, reporting units, manufacturers, and products.
Signal Detection: Detects new adverse reaction signals through data mining algorithms.
Report Generation: Quickly generates various statistical reports and charts.
User-Friendly Interface: Simple and intuitive operation.


## Data Files and Extension Libraries
EasyPV relies on several data files and extension libraries to function properly. These files contain configuration data and compiled modules that are essential for the program's operation. The following files are included with the program: sor.zip,def.zip,easypv.so/easypv.pyd,easystat.so/easystat.pyd,adrmdr.so/adrmdr.pyd, and pinggutools.so/pinggutools.pyd.
These files are integral to the functionality of EasyPV and must be present in the appropriate directories for the program to operate correctly.
Some of the database files included in the above archives are encrypted to ensure data security and integrity. However, EasyPV is designed to automatically decrypt these files during runtime, so no manual intervention is required. The decryption process is seamless and ensures that the program can access the necessary data without compromising security.
Important Note: The data files provided during installation are essentially blank. Upon the first launch of the program, EasyPV will download updates from a third-party source to populate these files. You must accept this download and update process to use the software.

## Installation

You can install EasyPV via PyPI:

pip install easypv
AdeptML 1.3.0
A High-Level PyTorch based Library for Hybrid Physics-Informed Machine Learning Models
Requires Python: >=3.9.0

# Auto-differentiable embedding of Physics and Torch Machine Learning (AdePT-ML):

This is a convienience library built on top of PyTorch to enable easy integration and training of hybrid models involving physics and deep learning modules. 

### Features
1. Allows integration of torch.nn.module with numpy functions and enable training with torch optimizers.
1. Pre-defined Modules and configs for physics and MLP architectures.
5. Integrated training function with tensorboard support.

## Installation
Installing with pip
```
pip install adeptml 
```
### Requirements (Automatically installed with pip): 
1. PyTorch (https://pytorch.org/)
2. Joblib (https://joblib.readthedocs.io/en/latest/) (For loading and saving model parameters)
3. Tensorboard

## Usage:

The primary building block of this package is the [Hybrid Model]() class. It neatly packages all the member models into one main Torch model and enables running forward inference as well as backpropagation.
The class accepts as input an instance of the [Hybrid Config]() class. This config is useful in defining all the constituent modules and their inputs.

As component modules, the [Models]() module provides a straight forward [MLP]() implementation as well as a [Physics Module]().
This module is a torch Autograd wrapper which enables the integration of non-Torch numpy functions into a fully torch model and allows for training with torch optimizers.

## API Documentation:
Visit our [Read the docs page](https://adept-ml.readthedocs.io/en/latest/)

## Examples:
Refer to the tests file. Additional examples will be added soon.
folder_indexer 0.2.3
A Python script to index a large folder structure into a parquet file, along with metadata
Requires Python: >=3.10

# folder-indexer-py
A Python script to index a large folder structure into a parquet file, along with metadata

## Description

This script is useful for searching for files stored on a reasonably slow disk
from backups, especially in where you aren't sure about the files are are searching for.

Use tools like DBeaver and DuckDB to query and explore the generated index.

## Usage

```bash
uv tool install folder_indexer

folder_indexer -i /path/to/input/folder -o /path/to/output/folder
```

## Metadata Indexed and Output

The output parquet file (`file_index.parquet`) has the following columns:

* file_path
* folder_path
* file_name
* file_size_bytes
* entry_kind
* md5_hex
* sha256_base64
* date_created
* date_modified
* magic_file_type_1
* first_100_bytes
* last_100_bytes
* timestamp_crawled
* indexing_start_timestamp