semvec/pypi-packages huggingface dataset:
arcturus-buildkit 1.6.0
A powerful, modular NLP toolkit for Python.

\# Arcturus BuildKit



Arcturus BuildKit is a \*\*lightweight, pure Python NLP and supervised ML chatbot framework\*\*.  

It combines \*\*ArcturusNLP\*\* for text preprocessing with \*\*ArcturusSUPERVISED\_INTENT\*\* for intent-based chat responses.



\## Features



\- Tokenization, lemmatization, and POS tagging  

\- Multiword entity recognition with fuzzy matching  

\- Synonyms and n-grams  

\- Intent-based supervised ML for chatbot responses  

\- Pure Python, no external libraries required  



\## Installation



```bash

pip install arcturus-buildkit
pathsdata-distributed 43.0.0
PathsData Distributed Query Engine - Python client for distributed SQL execution
Requires Python: >=3.9
Keywords: pathsdata, distributed, sql, query-engine, ballista, rust

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# PathsData Distributed

PathsData Distributed Query Engine - Python client for distributed SQL execution.

This project is versioned and released independently from the main Ballista project and is intentionally not
part of the default Cargo workspace so that it doesn't cause overhead for maintainers of the main Ballista codebase.

## Creating a SessionContext

> [!IMPORTANT]
> Current approach is to support datafusion python API, there are know limitations of current approach,
> with some cases producing errors.
> We trying to come up with the best approach to support datafusion python interface.
> More details could be found at [#1142](https://github.com/apache/datafusion-ballista/issues/1142)

Creates a new context and connects to a Ballista scheduler process.

```python
from pathsdata_distributed import BallistaBuilder
>>> ctx = BallistaBuilder().standalone()
```

### Example SQL Usage

```python
>>> ctx.sql("create external table t stored as parquet location './testdata/test.parquet'")
>>> df = ctx.sql("select * from t limit 5")
>>> pyarrow_batches = df.collect()
```

### Example DataFrame Usage

```python
>>> df = ctx.read_parquet('./testdata/test.parquet').limit(5)
>>> pyarrow_batches = df.collect()
```

## Scheduler and Executor

Scheduler and executors can be configured and started from python code.

To start scheduler:

```python
from pathsdata_distributed import BallistaScheduler

scheduler = BallistaScheduler()

scheduler.start()
scheduler.wait_for_termination()
```

For executor:

```python
from pathsdata_distributed import BallistaExecutor

executor = BallistaExecutor()

executor.start()
executor.wait_for_termination()
```

## Development Process

### Creating Virtual Environment

```shell
python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt
```

### Building

```shell
maturin develop
```

Note that you can also run `maturin develop --release` to get a release build locally.

### Testing

```shell
python3 -m pytest
```