semvec/pypi-packages huggingface dataset:
llmbanter 0.6.0
LLM Banter: See 2 large language models (LLMs) banter, debate and chat with each other.
License: MIT
Requires Python: >=3.9,<4.0
Keywords: LLM

# LLM Banter

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LLM Banter: See 2 large language models (LLMs) banter, debate and chat with each other.

NOTE: This project is currently a WIP. Aiming for v1.0 release by end of March 2024.

![Two AI Robots in discussion](https://github.com/dylanhogg/llmbanter/blob/main/docs/img/header.jpg?raw=true)

## Installation

You can install [llmbanter](https://pypi.org/project/llmbanter/) using pip, ideally into a Python [virtual environment](https://realpython.com/python-virtual-environments-a-primer/#create-it).

```bash
pip install llmbanter
```

## Usage Examples

Have 2 bots chat with each other:

```bash
llmbanter expert/psychiatrist famous/einstein
```

Talk to a Python programming expert:

```bash
llmbanter expert/python human
```

Try jail breaking a bot to get it's system prompt etc:

```bash
llmbanter system/jailbreaker expert/python
```

Run a bot under a different LLM provider:

```bash
llmbanter TODO --TODO
```

This project is [MIT](https://github.com/dylanhogg/llmbanter/blob/main/LICENSE) licensed.
pycobol2parquet 0.0.3
A Python library to convert COBOL ebcdic file to parquet format based on copybook
License: MIT
Keywords: COBOL,parquet

# pycobol2parquet

pycobol2parquet is a Python library to convert COBOL ebcdic file to parquet format. 

I released [pycobol2csv](https://pypi.org/project/pycobol2csv/) back in 2021 and it has been deployed to multiple production systems. One feedback I received is about the possibility of converting from Cobol to Parquet directly for analytical workload. 

It is straightforward to reuse the same underline knowledge and code to generate Parquet file.


Install the python module:

`pip install pycobol2parquet`

To use the module:

```
from pycobol2parquet import convert_cobol_file, decode_copybook_file

row_length, cobol_struc = decode_copybook_file(copybook_file)

convert_cobol_file(copybook_file, data_file, output_file, codepage, debug=False)

```

- copybook_file: copybook filename
- data_file: data filename 
- output_file: output parquet filename
- codepage: codepage for edibic, refer to https://docs.python.org/3.7/library/codecs.html#standard-encodings for details
- debug: enable for more debug information, default is OFF

Please refer to _convert_cobol_test_main.py_ for details. 

## test 

2 sets of test data have been created from scratch. Each set includes a copybook and an EBCDIC data file.

To test:

```
python convert_cobol_test_main.py --copybook testdata\test2\DWSTUB.txt --data testdata\test2\DWSTUB_DATA.DAT --output DWSTUB_DATA_output.parquet
```

## known issues and limitations

- Be aware of the resources available in your runtime environment and make sure the Cobol file size is not beyond the limit or cause any performance issue.

To handle large Cobol files, you can split the files into smaller chunks and then process the chunks in parallel. Please refer to the [medium post](https://medium.com/@jasonli.lijie/process-large-cobol-files-efficiently-with-pycobol2csv-pycobol2parquet-f023533607e4) for details.

- When creating Parquet files the library detects data type automatically. This is to simplify the parameters passed to the conversion function.

<!-- Repo: https://github.com/jasonli-lijie/pycobol2parquet -->