semvec/pypi-packages huggingface dataset:
neuraltrust-sdk 0.2.25
Python SDK to configure and run evaluations for your LLM-based application
Requires Python: <3.13,>=3.10

# NeuralTrust SDK

The NeuralTrust SDK provides tools for generating test sets, creating knowledge bases, and running evaluations for language models.

## Table of Contents

- [Installation](#installation)
- [Key Components](#key-components)
- [Usage Example](#usage-example)
- [Key Classes](#key-classes)
  - [KnowledgeBase](#knowledgebase)
  - [GenerateTestset](#generatetestset)
  - [EvaluationSet](#evaluationset)
- [Advanced Usage](#advanced-usage)
- [License](#license)

## Installation

```bash
pip install neuraltrust-sdk
```


## Key Components

1. **KnowledgeBase**: Create and manage knowledge bases from various sources.
2. **GenerateTestset**: Generate test sets for evaluations.
3. **EvaluationSet**: Set up and run evaluations.

## Usage Example

Here's an example of how to use the NeuralTrust SDK:

```python
    import os
    from src.neuraltrust.api_keys.neuraltrust_api_key import NeuralTrustApiKey
    from src.neuraltrust.api_keys.openai_api_key import OpenAiApiKey
    from src.neuraltrust.evaluation_set import EvaluationSet
    from src.neuraltrust.generate_testset import GenerateTestset
    from src.neuraltrust.generators import KnowledgeVectorBase
    # Set API keys
    NeuralTrustApiKey.set_key(os.getenv('NEURALTRUST_API_KEY'))
    OpenAiApiKey.set_key(os.getenv('OPENAI_API_KEY'))
    # Create a Knowledge Base from PDF and upload to Azure AI Search
    knowledge_base = KnowledgeVectorBase.from_pdf(
        'data/banking/',
        search_service_name="neuraltrust-search",
        index_name="banking",
        api_key=os.getenv('AZURE_AI_SEARCH_API_KEY')
    )
    # Generate a test set
    testset = GenerateTestset(
        evaluation_set_id="faqs_ab6be3",
        num_questions=20,
        knowledge_base=knowledge_base
    )
    testset_id = testset.generate()
    # Create and run an evaluation set
    eval = EvaluationSet(id="faqs_ab6be3")
    eval.run()
```

## Key Classes

### KnowledgeBase

The `KnowledgeBase` class allows you to create and manage knowledge bases from various sources, including pandas DataFrames, Azure AI Search, and PDF files.

#### Methods:
- `from_pandas(df: pd.DataFrame, columns: Optional[Sequence[str]] = None, **kwargs) -> KnowledgeBase`
- `from_pdf(path: str, search_service_name: str, index_name: str, api_key: str, **kwargs) -> KnowledgeVectorBase`

### GenerateTestset

The `GenerateTestset` class is used to generate test sets for evaluations based on a knowledge base.

#### Methods:
- `generate() -> str`: Generates a test set and returns the testset ID.

### EvaluationSet

The `EvaluationSet` class is used to set up and run evaluations.

#### Methods:
- `run()`: Runs the evaluation set.

## Advanced Usage

For more advanced usage and configuration options, please refer to the individual class documentation.
douroucoulisay 0.1.1.8.1
A package to print messages with douroucouli ASCII art and sounds.

# Douroucoulisay

`douroucoulisay` is a Python package that lets you print messages with delightful douroucoulis ASCII art. Perfect for adding a touch of whimsy to your command-line outputs! It is a HOOT and a WHOOP to use.

## Features

- Fetch and display douroucoulis ASCII art.
- Create speech bubbles around your messages.
- Easy to use from the command line.

## Installation

## You can install the package from PyPI using pip:

#```sh
#pip install douroucoulisay

# Usage
 Once installed, you can use douroucoulisay from the command line to print messages with douroucouli ASCII art.

# Basic Usage
To display a message with the default douroucouli:
douroucoulisay "Hello, world!"

# Specify Douroucouli
To use a specific douroucouli ASCII art, use the -d option followed by the douroucouli name:
douroucoulisay "How are you?" -d douroucouli2

# Available Douroucouli
Ensure that the douroucouli you want to use exists in the assets/ directory of the package. By default, the package includes:

douroucouli1
douroucouli2
douroucouli3

Example
Here’s an example of what the output looks like:


  ----------------------------------
 < Hi, I'm douroucoulisay, and you? >
  ----------------------------------
                                            
               ############-            
           ##############--###+         
        ###-.+##############.-###       
      ##+-.####-##############.-####    
   ####+-####  +########-  ####+--+###  
  ##+---###      ######     +###--++-#  
  #---+###  ....  ###+  ...   ###-+-+#  
  #+-+###  .      ###         -##+-##-  
   #-###  .######.###. #####-  ###--#.  
   #-### -#     .######-    ## .##--#   
   ##### +.       #++#.      #--##-##   
    #+## .#      .#+##-      # +##-#    
    ##### ##+  -###++###-  ### ##-##    
     #####  +######++#######  #####     
      ##+##     ##++++##     ###+#      
       ##-##.######+#######-+#-##       
        -###- -##########+  +###        
          ###-  #######+  .###-         
           .###.        -###            
              ###########+
siglent-sds 0.2.5
Interface to the Siglent SDS series of oscilloscopes.
Requires Python: <3.13,>=3.9

Siglent_SDS
===========

Siglent_SDS is a python interface to Siglent oscilloscopes. It has been tested on the Siglent
SDS800X HD series, but may also be compatible with similar Siglent oscilloscope models such as:

- Siglent SDS5000X
- Siglent SDS2000X Plus
- Siglent SDS6000 Pro
- Siglent SDS6000A
- Siglent SHS800X
- Siglent SHS1000X
- Siglent SDS2000X HD
- Siglent SDS6000L
- Siglent SDS1000X HD
- Siglent SDS7000A
- Siglent SDS3000X HD

The interface attempts to follow the specification in the [Siglent SDS Programming
Guide](https://siglentna.com/download/29924/?tmstv=1722402666).

It is intended to simplify configuring and acquiring waveforms from the device. Currently the only supported connection type is through network sockets (ethernet), but USB or other connection backends could be added with relatively little effort. When using network sockets, there is no dependency on VISA libraries or similar.

Documentation is located at https://ptapping.gitlab.io/siglent_sds

Source code is located at https://gitlab.com/ptapping/siglent_sds

Python Package Index (PyPI) page at https://pypi.org/project/siglent-sds
anacondacode 0.0.3.post5
anacondacode
Requires Python: >=3.7

### <img src="https://www.salabim.org/anacondacode/anacondacode_logo.png" align=left> anacondacode

This module contains one function: `runner`

The function `runner` is intended to be used in Anaconda code cell on an Excel workbook.
It has one parameter: `data`, which can be:

- a reference to an Excel range via the REF function (a list of lists)
- a pandas dataframe
- a numpy array

The data should refer to cells that contain valid Python code. In order to avoid problems, we recommend
to format these cells as text.
The result (if any) of the code should be put in the global `output`.

Each column has uses the same global AnacondaCode namespace. 
But, a column can also be used as were it a module. This is done by placing a

```# module = modulename```

line in the code column.
The given module name then becomes available for ordinary imports in the other code columns.
E.g.

<img src="https://www.salabim.org/anacondacode/anacondacode_poc.png" width=900 align=left>

The global variable `output` can be used to return an accepted value to the sheet (in this case a list of tuples).

You can download the above sheet at: www.salabim.org/anacondacode/anacondacode_poc.xlsx

For this and more examples, see the samples folder in the anaconda repository (www.github.com/salabim/anacondacode).

#### Installation
Install the module `anacondacode` from PyPI as described in section 'Managing software packages' of https://docs.anaconda.com/excel/code/

#### Disclaimer
This module is in no way affiliated with Anaconda, Inc.