semvec/pypi-packages huggingface dataset:
scrapeMM 0.5.5
Flexible multimodal scraper for social media and the open web.
Requires Python: >=3.10

# scrapeMM: Multimodal Web Retrieval
Simple web scraper to asynchronously retrieve webpages and access social media contents, fetching text along with media, i.e., images and videos.

This library aims to help developers and researchers to easily access multimodal data from the web and use it for LLM processing.

## Setup
* **If you want to download videos**: Then, the installation of [ffmpeg](https://ffmpeg.org/) is highly recommended.
In Conda, you can install it with `conda install -c conda-forge ffmpeg`.
* **If you want to scrape Perma.cc archive records**, you'll need to install playwright with `pip install playwright` and running `playwright install`.

## Usage
```python
from scrapemm import retrieve
import asyncio

url = "https://example.com"
loop = asyncio.get_event_loop()
result = loop.run_until_complete(retrieve(url))
result.render()
```
`scrapeMM` will ask you for the **API keys** needed for the social media integrations. You may skip them if you don't need them. 
You will also be prompted to choose a **password** that is used to secure the secrets in an encrypted file.

## How it works
```
Input:                                  Output:
URL (string)   -->   retrieve()   -->   MultimodalSequence
```
The `MultimodalSequence` is a sequence of Markdown-formatted text and media provided by the [ezMM](https://github.com/multimodal-ai-lab/ezmm) library.

Web scraping is done with [Firecrawl](https://github.com/mendableai/firecrawl) and [Decodo](https://decodo.com/).

## Supported Platforms
### Social Media
- ✅ X/Twitter
- ✅ Telegram
- ✅ Bluesky
- ✅ TikTok
- ✅ YouTube
- (✅️) Instagram: works for most content
- ⏳ Facebook: done for videos but not for images yet
- ❌ Threads: TBD
- ❌ Reddit: TBD

### Archiving Services
- ❌ Perma.cc
- ❌ Archive.today
- ❌ Wayback Machine, Internet Archive (web.archive.org)
- ❌ AwesomeScreenshot.com
- ⏳ MediaVault (mvau.lt): Works for images but not for videos yet
- ❌ Ghost Archive (ghostarchive.org)
aifoundary 1.0.2
Enterprise AI Compliance Toolkit with PII Detection and Secure RAG
Requires Python: >=3.9

# Aifoundary

**Enterprise AI Compliance Toolkit with PII Detection and Secure RAG Guardrails**

<<<<<<< HEAD
Development
Bashpip install -e .[dev]
pytest
## Deterministic RAG Governance

Aifoundary enforces Retrieval-Augmented Generation using policy-as-code.

### Features
- Prompt injection blocking
- PII detection & redaction
- Multi-context grounding checks
- Explainable coverage failures
- Auto-rewrite + guarded retry
- Signed audit logs
- CI/CD gateable decisions

### Quick demo
```bash
aifoundary rag-check --json prompt.txt context.txt || exit 1


In `pyproject.toml`:

```toml
version = "1.0.1"
=======
Aifoundary is a production-ready Python library designed to help teams deploy AI systems safely in regulated environments. It focuses on **data protection, decision transparency, and retrieval-augmented generation (RAG) safety**.

The library is framework-agnostic and can be integrated into existing AI pipelines without replacing your models or infrastructure.

---

## Key Capabilities

- **PII Detection**
  - Hybrid detection using deterministic rules and ML-based entity recognition
  - Designed for logs, prompts, documents, and model inputs

- **Secure RAG Validation**
  - Guards against unsafe context injection
  - Ensures retrieved documents comply with policy constraints
  - Prevents accidental data leakage in generation flows

- **Audit-First Design**
  - Deterministic behavior
  - Clear failure modes
  - Designed to integrate with enterprise audit and governance systems

- **Minimal, Explicit API**
  - No hidden side effects
  - No model hosting
  - No vendor lock-in

---

## Installation

```bash
pip install aifoundary
Optional FAISS Support (Linux recommended)
bash
Copy code
pip install "aifoundary[faiss]"
Note: FAISS requires native compilation and may not install on macOS.

Command Line Interface
Aifoundary includes a lightweight CLI for validation and diagnostics.

bash
Copy code
aifoundary doctor
aifoundary scan sample.txt
aifoundary rag-check prompt.txt context.txt
Intended Use Cases
AI compliance and governance teams

Regulated industries (fintech, healthcare, enterprise SaaS)

Secure RAG pipelines

Internal AI platform teams

Author
Chandan Galani
Email: galanichandan@gmail.com
Phone: +91-9326176427

License
Apache 2.0
>>>>>>> 152c6d9 (v1.0.1: policy-as-code, explainable RAG, audit chain, simulation mode)
fragpipe-speclib 0.1.58
FragPipe-SpecLib: Simple library generation for FragPipe

FragPipe-SpecLib: Simple library generation for FragPipe
================================================

FragPipe-SpecLib is a fork of [EasyPQP](https://github.com/grosenberger/easypqp). It is used by [FragPipe](https://github.com/Nesvilab/FragPipe) to build spectral library.

Installation
============

The Windows version of FragPipe has FragPipe-SpecLib bundled.
For the Linux version, install the production version:

```
pip install FragPipe-SpecLib
```

Install the development version from GitHub:

````
pip install git+https://github.com/Nesvilab/FragPipe-SpecLib.git@master
````
gamslib 0.7.12
Modules and subpackages used in various GAMS5 related projects
Requires Python: >=3.11
Keywords: GAMS

# gamslib

Gamslib is a collection of GAMS related modules and packages, which are used in multiple other packages.

## Installation

gamslib is available on pypi.org and can be installed via pip:

```
pip install gamslib
```

## Usage

As gamslib is a library, it can only we used with other code. 

The main purpose is to make code reusable in other GAMS5 projects and to have
a unique way of doing things. If you are not working on GAMS5 reated code
(which is very likely), this library will be useless for you.

Currently these subpackages are available (more to come):

## objectcsv

Handle object and datastream metadata in object csv files.

When creating bags for GAMS, we provide some metadata in csv
files (which are not part of the bag, btw).

The objectcsv package provides tools to handle this metadata.

  * The ObjectCSV class represents the object    and datastream csv data for a
    single object. It is created by providing the path to the  object
    directory. 
  * The manage_csv module can be used to collect csv data from all objects into
    a single file, which makes editing the data more efficient. It also has a
    function to update the csv files in the object directories based on the
    collected data.
  * The xlsx module can be used to convert the csv files to xlsx files and vice
    versa. This is useful for editing the data in a spreadsheet without the
    hassles of importing and exporting the csv files, which led to encoding
    problems in the past.

## projectconfiguration

This package contains a central class `Configuration` that represents the
project configuration. To create this object, the function
`load_configuration(OBJECT_ROOT, [PATH_TO_TOML_FILE])` should be used.

The function tries to find the project configuration file, validates its
content, and creates the central Configuration object with all sub-objects
(Each TOML inline table is provided as its own sub-object). These sub-objects
are currently:

  * general
  * metadata

A basic configuration file can be generated via the `create_condiguration()`
function.


## Contributing

The Github repository is ment to be a read only mirror of the work repository
hosted on our institutional private Github server. You can use the bug tracker
on Github, but everything else should happen in the Zimlab Github repo.


## License

[MIT](https://choosealicense.com/licenses/mit/)