semvec/pypi-packages huggingface dataset:
markitdown1 0.1.1
Batch convert documents to Markdown using MarkItDown
Requires Python: >=3.8

# markitdown1

A simple CLI tool and library to batch convert documents into Markdown using `markitdown`.

## Install
```bash
pip install markitdown1
```

## CLI Usage
```bash
markitdown1 src output/markdown
```

## Python Usage
```python
from markitdown1 import convert_all_to_md
convert_all_to_md("src", "output/markdown")
```
arango-taxii-server 1.1.5
A lightweight TAXII API wrapper for ArangoDB.
Requires Python: >=3.9

# Arango TAXII Server

[![codecov](https://codecov.io/gh/muchdogesec/arango_taxii_server/graph/badge.svg?token=FYX14J4PSE)](https://codecov.io/gh/muchdogesec/arango_taxii_server)

## Overview

![](docs/arango_taxii_server.png)

Arango TAXII Server is a production ready implementation of a TAXII 2.1 Server designed to work with ArangoDB.

## Install

### Download and configure

```shell
# clone the latest code
git clone https://github.com/muchdogesec/arango_taxii_server
cd arango_taxii_server
```

### Pre-requisites

**IMPORTANT**: ArangoDB and Postgres must be running. These are not deployed in the compose file.

If you are not sure what you are doing here, [follow the basic setup steps here](https://community.dogesec.com/t/best-way-to-create-databases-for-obstracts/153/2).

### Configuration options

Arango TAXII Server has various settings that are defined in an `.env` file.

To create a template for the file:

```shell
cp .env.example .env
```

To see more information about how to set the variables, and what they do, read the `.env.markdown` file.

### Build the Docker Image

```shell
sudo docker compose build
```

### Start the server

```shell
sudo docker compose up
```

### Access the server

The webserver (Django) should now be running on: http://127.0.0.1:8009/

You can access the Swagger UI for the API in a browser at: http://127.0.0.1:8009/api/schema/swagger-ui/

## Useful supporting tools

* [TAXII 2.1 Specification](https://docs.oasis-open.org/cti/taxii/v2.1/taxii-v2.1.html)
* [STIX 2.1 Specification](https://docs.oasis-open.org/cti/stix/v2.1/stix-v2.1.html)

## Other TAXII server implementations

We realise not everyone will want to use ArangoDB. Here are some other open-source TAXII server options you could consider;

* [OASIS TC Open Repository: TAXII 2 Server Library Written in Python](https://github.com/oasis-open/cti-taxii-server)
* [A cyber threat intelligence server based on TAXII 2 and written in Golang](https://github.com/freetaxii/server)
* [TAXII server implementation in Python from EclecticIQ](https://github.com/eclecticiq/OpenTAXII)

## Support

If you are a paying DOGESEC customer, please contact our support team.

For everyone else, [minimal support provided via the DOGESEC community](https://community.dogesec.com/).

## License

[Apache 2.0](/LICENSE).
Orange3-Associate 1.4.0
Orange add-on for mining frequent itemsets and association rules.
Requires Python: >=3.9
Keywords: frequent itemset mining, frequent pattern mining, association rules, apriori, fp-growth, frequent patterns, FIM, FPM, orange3 add-on

Orange3-Associate
=================

Orange add-on for enumerating frequent itemsets and association rules mining.

See [documentation](http://orange3-associate.readthedocs.org/).


Features
--------
#### Association Rules
* induce association and classification rules
* filter rules by the antecedent or consequent part

#### Frequent Itemsets
* find frequent itemsets
* set criteria for search
* filter the results with regular expression
iflow-mcp_scimcp_owl-mcp 0.1.4
A MCP server for OWL ontology operations
Requires Python: <=3.13,>=3.10

# OWL-MCP

OWL-MCP is a Model-Context-Protocol (MCP) server for working with Web Ontology Language (OWL) ontologies. 

![img](https://avatars.githubusercontent.com/u/182288589?s=200&v=4)
![img](https://avatars.githubusercontent.com/u/4671070?s=200&v=4) 

## Quick Start

This walks you through using owl-mcp with [Goose](https://github.com/block/goose), but any MCP-enabled AI host will work.

### Install Goose

You can use either the Desktop or CLI version of Goose from here:

* [goose installation](https://block.github.io/goose/docs/getting-started/installation/)

Follow the instructions for setting up an LLM provider (Anthropic recommended)

### Install OWL-MCP extension

You can either install directly from this link:

 * [Install OWL-MCP](goose://extension?cmd=uvx&arg=owl-mcp&id=owl_mcp&name=OWL%20MCP)

 Or to do this manually, in the Extension section of Goose, add a new entry for owlmcp:

 `uvx owl-mcp`

 This video shows how to do this manually:

 [![configuration](https://img.youtube.com/vi/509qVPEbv0Q/0.jpg)](https://www.youtube.com/watch?v=509qVPEbv0Q)


 
### Try it out

You can ask to create an ontology, and add axioms to an ontology:

[![editing](https://img.youtube.com/vi/sAXs3djX854/0.jpg)](https://www.youtube.com/watch?v=sAXs3djX854)




## How this works

The MCP server provides function calls for finding, adding, or removing OWL axioms, using OWL functional syntax. Each function call is accompanied by the file path of the OWL file on your disk. Any format supported by py-horned-owl is accepted (we following OBO guidelines and recommend functional syntax for source).

The server takes care of keeping an instance of the ontology in memory and syncing it with disk. Any CRUD operation simultaneously updates the in-memory model and syncs this with disk. If you have Protege running, Protege will also
sync with local disk, and show updates.

The server is well adapted for working with OBO-style ontologies - when OWL strings are sent back to the client, labels for opaque IDs are included after `#`s comments, as is common for obo-format.

## Key Features

- **MCP Server Integration**: Connect AI assistants directly to OWL ontologies using the standardized Model-Context-Protocol
- **Thread-safe operations**: All ontology operations are thread-safe, making it suitable for multi-user environments
- **File synchronization**: Changes to the ontology file on disk are automatically detected and synchronized
- **Event-based notifications**: Register observers to be notified of changes to the ontology
- **Simple string-based API**: Work with OWL axioms as strings in functional syntax without dealing with complex object models
- **Configuration system**: Store and manage settings for frequently-used ontologies
- **Label support**: Access human-readable labels for entities with configurable annotation properties