semvec/pypi-packages huggingface dataset:
generative-ai-qa 0.1.1
A Python package for question answering using Google Generative AI.
License: MIT
Requires Python: >=3.8

# Generative AI QA

A Python package for question answering using Google Generative AI.

## Installation

Install the package using pip:

```bash
pip install generative_ai_qa

from generative_ai_qa import GenerativeAIQuestionAnswering

api_key = "YOUR_API_KEY"
model_name = "gemini-1.5-flash"
file_path = "data1.txt"

ai_qa = GenerativeAIQuestionAnswering(api_key, model_name, file_path)
ai_qa.load_data_from_file()
session_id = ai_qa.create_session()
ai_qa.ask_questions(session_id)
qcvisualiser 0.0.3
Visualiser for QuartiCal gain solutions.
License: MIT
Requires Python: <3.13,>=3.10

# QuartiCal-Visualiser

The QuartiCal-Visualiser is a convenient tool for visualising the gain solutions
produced by [QuartiCal](https://github.com/ratt-ru/QuartiCal). It also allows 
for interactive flagging of those solutions. 

## Installation

QuartiCal-Visualiser can be installed from PyPI by running
`pip install qcvisualiser`. Alternatively, developers can install it directly
from source using either Poetry or pip.

## Usage

QuartiCal-Visualiser can be run from the command line using
`govisualise path/to/gain`. You can then navigate to `localhost:5006` in
your browser to interact with the gains. 

## Options

At present, QuartiCal-Visualiser has limited options. All of them can be 
displayed by running `govisualise --help`.

## Remote Viewing

As QuartiCal starts a web server, it is possible to interact with it remotely.
This can be accomplished by port-forwarding e.g. 
`ssh -L 5006:localhost:5006 user@remote` before proceeding as detailed above.
savatarr-chatbot 1.0.2
A Python package for question answering using Google Generative AI.
License: MIT
Requires Python: >=3.8

# Generative AI QA

A Python package for question answering using Google Generative AI.

## Installation

Install the package using pip:

```bash
pip install generative_ai_qa

from generative_ai_qa import GenerativeAIQuestionAnswering

api_key = "YOUR_API_KEY"
model_name = "gemini-1.5-flash"
file_path = "data1.txt"

ai_qa = GenerativeAIQuestionAnswering(api_key, model_name, file_path)
ai_qa.load_data_from_file()
session_id = ai_qa.create_session()
ai_qa.ask_questions(session_id)