semvec/pypi-packages huggingface dataset:
scienceio 2.2.0
ScienceIO Python SDK
Requires Python: >=3.7

# ScienceIO

The official ScienceIO Python SDK for the ScienceIO API.

This package is available via [Pypi:scienceIO](https://pypi.org/project/scienceio/). View full documentation at [ScienceIO Docs](https://docs.science.io/docs).


## Usage and examples

1. Create a ScienceIO API key. Both the API key ID and the key secret will be
needed in order to set up the ScienceIO Python SDK for use.

2. Install the ScienceIO Python SDK.

   ```python
   pip install scienceio
   ```

3. Create a directory `.scienceio` in your home directory (Mac users, your home directory is `/Users/{username}`. Linux users, your home directory is `/home/{username}`. Windows users, your home directory is `C:\\Users\{username})` Inside this directory, create a text file called `config` containing your credentials:

   ```
   [SETTINGS]
   KEY_ID={your ScienceIO API key ID}
   KEY_SECRET={your ScienceIO API key secret}
   ```

4. Use the ScienceIO Python SDK to annotate text:

   ```python
   scio = ScienceIO()
   input_text = (
     'The COVID-19 pandemic has shown a markedly low proportion of '
     'cases among children 1–4. Age disparities in observed cases could be '
     'explained by children having lower susceptibility to infection, lower '
     'propensity to show clinical symptoms or both.'
   )

   results = scio.structure(input_text)
   print(results)
   ```
custom-guardrails 0.1.0
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.

NeMo Guardrails is an open-source toolkit for easily adding
    programmable guardrails to LLM-based conversational systems.
    Guardrails (or "rails" for short) are specific ways of controlling the output of an LLM,
    e.g., not talking about politics, responding in a particular way to specific user
    requests, following a predefined dialog path, using a particular language style,
    extracting structured data, etc.

    Key Benefits:

    - Building Trustworthy, Safe and Secure LLM Conversational Systems: The core value of
    using NeMo Guardrails is the ability to write fuzzy rails to steer select conversations.
    Developers can choose to define the behavior of their LLM-powered bots on certain
    topics and keep their creativity un-encumbered for others!

    - Connect models, chains, services, and more via actions: LLMs don't need to solve
    all the challenges. NeMo Guardrails provides the ability to connect your codebase or
    services to your chatbot seamlessly and securely!