semvec/pypi-packages huggingface dataset:
obanRAG 0.0.1
An advanced Retrieval-Augmented Generation system supporting PDF, Word, and Excel documents with feedback-based prioritization.
Requires Python: >=3.7

# obanRAG

`obanRAG` is an advanced Retrieval-Augmented Generation (RAG) library that supports text, PDF, Word, and Excel files. It leverages OpenAI's GPT-4 model to answer queries based on stored documents and dynamically prioritizes documents based on user feedback.

## Features

- **Document Handling**: Supports text, PDF, Word (.docx), and Excel (.xlsx) files.
- **Hybrid Search**: Combines semantic relevance and document priority to retrieve relevant content.
- **Feedback Loop**: Adjusts document priority based on user feedback to improve future responses.
- **Cache Management**: Caches query results for faster response times to repeated queries.

## Installation

Install the package via pip:

```bash
pip install obanRAG

Prerequisites:

OpenAI API Key: You need an OpenAI API key to interact with the GPT-4 model. Set this in the environment or pass it as a parameter when initializing the library.
Register at OpenAI to get an API key.
Note: Without an API key, the library wonâ€™t function as intended since it relies on OpenAI's services for query answering.

## Usage

Initialize the System

from obanRAG import vobanRAG

# Initialize the RAG system with your OpenAI API key
rag_system = vobanRAG(api_key="your_openai_api_key")

### Adding Documents
You can add documents of various formats to the document store. Each document type has a unique function.

## Adding Plain Text
Use add_text to add raw text data to the document store.

text_data = "This is a sample text document containing valuable important information."
rag_system.add_text(text_data, title="Sample Text Document")


## Adding a PDF Documents

pdf_path = "path/to/your/document.pdf"
rag_system.add_pdf(pdf_path)


## Adding a Word Document

word_path = "path/to/your/document.docx"
rag_system.add_word(word_path)


## Adding an Excel Document

excel_path = "path/to/your/spreadsheet.xlsx"
rag_system.add_excel(excel_path)


## Querying the System

query = "What is the main topic discussed in the document?"
answer = rag_system.process_query(query)
print("Answer:", answer)


## Providing Feedback

feedback_score = int(input("Please rate the quality of the answer (1-10): "))
rag_system.feedback_loop("document_title", feedback_score)
multi-interface-inversion 0.0.3
calculate gravity anomalies from multi-interfaces and estimate double-interfaces model from gravity anomalies, given prior information about constraints between the density interfacesthe using Crust1.0 model, where Vertical Gravity Gradient data are used asinputs
Requires Python: >=3.8

This packages is designed for the bathymetric model design and test.

The gravity anomaly is caused by the fluctuation of several density interfaces. Given the 
altitudes of these interfaces and the density assumption between those interfaces, the
vertical gravity anomalies shall be obtained, which is implied as the forward process in 
the Software. On the other hand, given the vertical gravity anomalies, some fluctuation of 
density interfaces shall be determined, which is implied as the inverse process in the 
Software.
cellxgene_lamin 0.3.2
Manage cellxgene metadata using LaminDB.
Requires Python: >=3.9

[![pypi](https://img.shields.io/pypi/v/cellxgene-lamin?color=blue&label=pypi%20package)](https://pypi.org/project/cellxgene-lamin)

# cellxgene-lamin: Manage cellxgene metadata using Lamin

Try it out with the public instance `laminlabs/cellxgene`:

- via LaminHub: https://lamin.ai/laminlabs/cellxgene
- via LaminDB API: `lamin load laminlabs/cellxgene`

Reference:

- [CELLxGENE](https://cellxgene.cziscience.com/)
- [CELLxGENE Census](https://chanzuckerberg.github.io/cellxgene-census/)
- [TileDB-SOMA](https://github.com/single-cell-data/TileDB-SOMA)
- [TileDB](https://github.com/TileDB-Inc/TileDB-Py)
django-design-system 0.0.2
Render Design System DESIGN_SYSTEM into a generic Django app
Requires Python: <4.0,>=3.8
Keywords: django

# Django-DesignSystem

Django-DesignSystem is an attempt at rendering a generique version of Django-Design-System. 

## Documentation

It is my conviction that the work done in DSFR (`"Système de design de l’État français" <https://www.systeme-de-design.gouv.fr/>`_)  and as visible in the Django-Design-System Python Package (`documentation (in French) <https://numerique-gouv.github.io/django-design-system/>`_) is fantastic. The only problem is that the website design itself is the French Government, and not any lamba website. 

So, unless you want the French Foriegn Legion knocking at your door (actually, it wouldn't be them, but they are well known enough to illustrate my point), we need the grace to accept a very intentional work to bettering web-mastery, readability and accessibility by all. 

France, though its' DiNum, or, actually I won't get into it, just be aware that French coolness also goes to OpenSource communities, has made something called "(`sites-failes <https://github.com/numerique-gouv/sites-faciles>`_)" as a Wagtail plugin. I think it's great! That's why DesignSystem.

As the French Government uses django-design-system for sites-faciles, let's use django-design-system for "(`wagtail-facile <https://github.com/chris2fr/wagtail-design-system>`_)".

## Requirements

(from django-design-system)

Tested with Python 3.8 to 3.11 and Django 3.2 to 5.0. Per `vermin <https://github.com/netromdk/vermin>`_, it should work with Python >= 3.6, and it should work with old versions of Django too.

Note: Only supported versions of Django and Python are supported by this project. You can check the supported versions and their end of life on the following pages:

- `Python <https://devguide.python.org/versions/>`_
- `Django <https://www.djangoproject.com/download/#supported-versions>`_

## Quick start

(from django-design-system)

See the `INSTALL.md <INSTALL.md>`_ file.