semvec/pypi-packages huggingface dataset:
findprob 0.2
A CLI that lets you classify and search for problems by topic using an LLM
Requires Python: >=3.11
Keywords: AI, LLM, classify problems, edtech, education, find problems, findprob, problems, search problems

# findprob

A CLI to classify and search for problems using LLMs

## Installation

1. Install the package: `pip install findprob`
2. (Optional) Install autocompletion: `findprob --install-completion`
3. Create an [OpenAI](https://platform.openai.com/signup) account and generate a new API key.
4. Create a [LangChain](https://smith.langchain.com/) account and generate a new API key and project.
5. Export the environment variables in your terminal:
```sh
LANGCHAIN_TRACING_V2=true
LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
LANGCHAIN_API_KEY=<YOUR API KEY HERE>
LANGCHAIN_PROJECT=<YOUR PROJECT NAME HERE>

OPENAI_API_KEY=<YOUR API KEY HERE>
```

## Usage

See the [official docs](https://phrdang.github.io/findprob/) or run `findprob --help` for more information.

## Credits

A research project for CS 194-271 at UC Berkeley (Fall 2024) created by:

- Rebecca Dang (rdang [at] berkeley [dot] edu)
- Jessica Lin (linjessica [at] berkeley [dot] edu)
- Samantha Huang (samanthahuang [at] berkeley [dot] edu)

Advised by:

- Professor Gireeja Ranade (ranade [at] eecs [dot] berkeley [dot] edu)
- Professor Narges Norouzi (norouzi [at] berkeley [dot] edu)
sql-upsert 0.1.2
A package for handling SQL upsert operations with pandas DataFrames
Requires Python: >=3.6

# SQL Upsert

A Python package for handling SQL upsert operations with pandas DataFrames.

## Installation

```bash
pip install sql_upsert
```

## Usage

```python
import sql_upsert as pdu
import pandas as pd
from sqlalchemy import create_engine

# Create engine
engine = create_engine('sqlite:///example.db')

# Create sample DataFrame
df = pd.DataFrame({'id': [1, 2], 'value': ['a', 'b']})

# Write with upsert functionality
pdu.to_sql_upsert(df, 'my_table', engine, unique_columns=['id'])
```

## Features

- Automatic handling of upsert operations
- Support for custom unique constraints
- Compatible with any SQLAlchemy supported database
- Preserves column types when creating tables

## Requirements

- Python >= 3.6
- SQLAlchemy >= 1.4.0
- pandas >= 1.0.0
skincare-chemistry 1.0.0
A library for analyzing skincare ingredients.
Requires Python: >=3.6

# Skincare Chemistry Library

Welcome to the Skincare Chemistry Library! This Python package is designed to help users analyze skincare ingredients based on scientific principles and improve their understanding of skincare formulations. It includes functionalities for assessing the properties of various ingredients and offering insights into their effectiveness and safety.

## Features

- **Ingredient Analysis**: Analyze individual ingredients' properties, benefits, and potential side effects.
- **Formulation Evaluation**: Assess the interaction of ingredients in skincare formulations.
- **Skin Type Matching**: Provide recommendations for ingredients based on different skin types (oily, dry, sensitive, etc.).

## Installation

To install the library, you can either download the source code directly or install it via `pip`.

### Option 1: Install from PyPI

You can install the library using `pip`:

```bash
pip install skincare-chemistry

git clone https://github.com/yourusername/skincare-chemistry.git
cd skincare-chemistry
pip install .

Documentation
For more detailed documentation, please visit the GitHub Wiki.

Contributing
We welcome contributions to improve the library. Please see the Contributing Guide for more information.

License
This project is licensed under the MIT License - see the LICENSE file for details.

Contact
For any questions or issues, please contact the project maintainers:

Anusha_Asim (anushaasim21@yahoo.com)
GitHub: venusflytrapfairy
simo-omics 1.0.0
SIMO: Spatial Integration of Multi-Omics single-cell datasets through probabilistic alignment
Requires Python: >=3.8

SIMO is an advanced Python package designed for the spatial integration of multi-omics data from single-cell datasets, utilizing probabilistic alignment techniques to accurately depict single-cell maps and reconstruct the spatial distribution of various biological molecules, thereby providing crucial insights into the complex multimodal heterogeneity and topological patterns of cells essential for advancing spatial biology.