semvec/pypi-packages huggingface dataset:
gmine 0.1.8
Text mining application

# Gmine
This program simulates the `where` expression in SQL using pandas.

The program filters data in a csv file based on if certain texts exist in some columns.
For example, one may want to find the data where the column "details of the story" contains the words "cat" and "dog", but not "wolf".
In this case, one may use the programme as follows:

    python -m gmine my_data.csv -c "details of the story" "( cat and dog ) and ( not wolf )"

The spaces between parenthesis and words are crucial.
The output of the programme is a file called `my_data_filtered.csv` which only holds data that satisfy the condition.
Alternatively, if one want to specify a custom output file name, one can use the `-o` flag followed by the output file name.
## Basic usage
    python -m gmine [-c COLUMN-NAME] [-o OUTPUT-FILE-NAME] <path_to_file> <cnf> 

If `-c` flag is not used, the default column name is `Report Event/Describe the facts of what happened`.
One may also write the command line argument into a text file,

```text
# my_options.txt

my_data.csv
roadside and waited and ( ' Regional Operations ' or ' regional operations ' or ROC )
-c
Report Event/Describe the story of what happened
-o
haha.csv
```

and simply type

    python3 -m gmine @my_options.txt

Notice that no quotation marks should be used for arguments provided in txt file unless it is used to group words in cnf.
openapi-agent 0.1.3
An OpenAPI agent that can execute operations based on natural language descriptions
Requires Python: <4.0,>=3.8

# OpenAPI Agent

A Python library that enables natural language interaction with any OpenAPI-compliant API. The agent interprets natural language commands, matches them to appropriate API operations, and executes them safely.

## Features

- 🤖 Natural language understanding of API operations
- 🔒 Secure code execution in sandboxed environments
- 🔄 Automatic retry mechanisms
- 💾 Caching of API specifications
- 🔍 Smart operation matching using tags and descriptions

## Installation

```bash
pip install openapi-agent
```

## Quickstart

```python
from openapi_agent import Agent

# Initialize agent with OpenAPI spec
agent = Agent('path/to/openapi.yaml', auth_header="Bearer your-token")

# Execute operations using natural language
result = agent.execute_function("github_agent", {
    "action": "Create a new user repository called 'my-repo' and return its full name"
})
```


## Security

- All code execution happens in isolated Docker containers
- Automatic timeout handling prevents infinite loops
- No file system access outside the sandbox
- Limited network access within the container

## Environment Variables

- `OPENAI_API_KEY`: Your OpenAI API key
- Additional API-specific authentication tokens as needed

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.