semvec/pypi-packages huggingface dataset:
boto3-pydantic-workspaces-web 1.0.2
Pydantic models for AWS Workspaces_web
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, workspaces_web

# boto3-pydantic-workspaces_web

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Workspaces_web.
boto3-pydantic-workspaces-thin-client 1.0.2
Pydantic models for AWS Workspaces_thin_client
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, workspaces_thin_client

# boto3-pydantic-workspaces_thin_client

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Workspaces_thin_client.
pubmed-paper-fetcher 0.1.0
Fetch and filter PubMed papers based on pharmaceutical/biotech affiliations.
Requires Python: >=3.6

# 📚 PubMed Paper Fetcher - Python Package

A Python program to fetch research papers from PubMed based on a user-specified query. It identifies papers with at least one author affiliated with pharmaceutical or biotech companies and returns the results as a CSV file.

---

## 🌟 Features
- 🔍 Fetches papers from PubMed API using full query syntax.
- 📄 Outputs results as a CSV file with details:
  - **PubmedID**: Unique identifier for the paper.
  - **Title**: Title of the paper.
  - **Publication Date**: Date of publication.
  - **Non-academic Authors**: Authors affiliated with non-academic institutions.
  - **Company Affiliations**: Names of pharmaceutical/biotech companies.
  - **Corresponding Author Email**: Email of the corresponding author.
- 📦 Packaged with Poetry.
- 📤 Published on TestPyPI.

---

## 📁 Project Structure
```
📂 pubmed_paper_fetcher/
│
├── 📄 __init__.py      (Makes it a package)
├── 📄 cli.py           (Command-line interface logic)
├── 📄 fetcher.py       (Module containing core functionalities)
```

---

## ⚙️ Installation

### Clone the Repository
```bash
$ git clone https://github.com/YourUsername/pubmed-paper-fetcher-supri267.git
$ cd pubmed-paper-fetcher-supri267
```

### Install Dependencies
Ensure you have Poetry installed. If not, install via:
```bash
$ pip install poetry
```

Install project dependencies:
```bash
$ poetry install
```

---

## 🚀 Usage

### Command-Line Interface
1. **To fetch papers and display them on console:**
```bash
$ poetry run get-papers-list "COVID-19 vaccine"
```

2. **To save results to a CSV file:**
```bash
$ poetry run get-papers-list "COVID-19 vaccine" -f results.csv
```

3. **To enable debug mode for detailed logs:**
```bash
$ poetry run get-papers-list "COVID-19 vaccine" -d
```

---

## 🔧 Testing the Module (TestPyPI)
Create a virtual environment:
```bash
$ python -m venv test-env
$ .\test-env\Scripts\activate  (Windows)
$ source test-env/bin/activate    (Linux/Mac)
```

Install the package:
```bash
$ pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple pubmed-paper-fetcher-supri267
```

Run the tool:
```bash
$ get-papers-list "Machine learning" -d
```

---

## 📚 Tools & Libraries Used
- **Python** (>=3.8)
- **Requests** (API interaction)
- **Poetry** (Dependency management & Packaging)
- **Twine** (Publishing to TestPyPI)

---

## 📌 Helpful Links
- [PubMed API Documentation](https://www.ncbi.nlm.nih.gov/books/NBK25499/)
- [Poetry Documentation](https://python-poetry.org/docs/)
- [TestPyPI](https://test.pypi.org/)

---

## 📢 Contact
For any issues or suggestions, feel free to reach out!
boto3-pydantic-workspaces 1.0.2
Pydantic models for AWS Workspaces
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, workspaces

# boto3-pydantic-workspaces

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Workspaces.
boto3-pydantic-workmailmessageflow 1.0.2
Pydantic models for AWS Workmailmessageflow
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, workmailmessageflow

# boto3-pydantic-workmailmessageflow

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Workmailmessageflow.
boto3-pydantic-workmail 1.0.2
Pydantic models for AWS Workmail
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, workmail

# boto3-pydantic-workmail

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Workmail.
ai-labs-snippets-sdk 3.5.0
AI Labs Snippets SDK for Python
Requires Python: >=3.6

# AI Labs Snippets SDK

Python SDK for interacting with AI Labs services

## 安装

```bash
pip install ai-labs-snippets-sdk
```

## 快速开始

```python
import ai_labs_snippets_sdk

print(ai_labs_snippets_sdk.model)  # 加载模型设置环境
```
boto3-pydantic-workdocs 1.0.2
Pydantic models for AWS Workdocs
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, workdocs

# boto3-pydantic-workdocs

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Workdocs.
boto3-pydantic-wisdom 1.0.2
Pydantic models for AWS Wisdom
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, wisdom

# boto3-pydantic-wisdom

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Wisdom.
boto3-pydantic-wellarchitected 1.0.2
Pydantic models for AWS Wellarchitected
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, wellarchitected

# boto3-pydantic-wellarchitected

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Wellarchitected.
boto3-pydantic-wafv2 1.0.2
Pydantic models for AWS Wafv2
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, wafv2

# boto3-pydantic-wafv2

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Wafv2.
etllog 0.1.1
Universal logger with custom levels for ETL and automation processes.
Requires Python: >=3.7

# etllog

`etllog` is a universal logger designed for ETL pipelines, automation tasks, and web scraping scripts in Python.

This library is built on top of Python's standard `logging` module and the excellent `colorlog` package. Full credit and gratitude go to the developers of those foundational libraries.

The main goal of `etllog` is to provide custom log levels that make it easier to structure and monitor all stages of your data processes. These levels help you track your pipeline with meaningful, semantically distinct messages.

By default, the following custom levels are included:

| Level        | When to Use                                         | Color        |
|--------------|------------------------------------------------------|--------------|
| `STARTUP`    | When initializing or starting a process              | Bold Blue    |
| `VALIDATION` | When validating data, schema, or credentials         | Blue         |
| `DRY_RUN`    | For simulation runs that don't commit any changes    | Purple       |
| `SUCCESS`    | When a process completes successfully                | Bold Green   |
| `INFO`       | For general runtime information                      | Green        |
| `WARNING`    | When something unexpected happens, but the process continues | Yellow  |
| `ERROR`      | When a failure occurs but the system stays alive     | Red          |
| `CRITICAL`   | When a critical error occurs and the process halts   | Bold Red     |
| `DEBUG`      | For detailed technical/debugging messages            | Cyan         |

---

## Installation

Install via pip:

```bash
pip install etllog
boto3-pydantic-waf-regional 1.0.2
Pydantic models for AWS Waf_regional
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, waf_regional

# boto3-pydantic-waf_regional

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Waf_regional.
boto3-pydantic-waf 1.0.2
Pydantic models for AWS Waf
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, waf

# boto3-pydantic-waf

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Waf.
boto3-pydantic-vpc-lattice 1.0.2
Pydantic models for AWS Vpc_lattice
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, vpc_lattice

# boto3-pydantic-vpc_lattice

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Vpc_lattice.
calculadora-test 0.0.1
Um pacote simples de calculadora em Python
Requires Python: >=3.5

# Calculadora

## DescriÃ§Ã£o
A biblioteca **calculadora** permite realizar operaÃ§Ãµes matemÃ¡ticas bÃ¡sicas, como soma, subtraÃ§Ã£o, multiplicaÃ§Ã£o e divisÃ£o, de forma simples e eficiente.

## InstalaÃ§Ã£o
Para instalar o pacote, utilize o gerenciador de pacotes [pip](https://pip.pypa.io/en/stable/):

```bash
pip install calculadora
````

# Uso
Aqui estÃ¡ um exemplo de como utilizar a biblioteca:
```
from calculadora_test import soma, subtracao, multiplicacao, divisao

# Exemplo de uso:
resultado = soma(3, 4)
print(resultado)  # SaÃ­da: 7
```

# Autor
Pablo

# LicenÃ§a
MIT
vola 0.0.1
Placeholder for the vola python package, from the Vola Analytics Library. To obtain a copy of vola, please contact Vola Dynamics LLC
Requires Python: >=3.8

# vola python module

Copyright (c) 2025 Vola Dynamics LLC - All Rights Reserved

This package is a placeholder for the vola python module. To obtain a copy of
vola, please contact Vola Dynamics LLC.

https://voladynamics.com/

Note: if you are hosting a pypi server, and the server has begun defaulting to
pypi.org as it's source for vola, the recommended steps are to either:

1. Constrain the version you're installing (eg, use `vola>=3.20` when installing
   the package), or,
2. Disable fallback to pypi.org for your local package repository. For example,
   [`pypi-server`][pypi-server] allows disabling fallback with
   `--disable-fallback`.

[pypi-server]: https://pypi.org/project/pypiserver/
boto3-pydantic-voice-id 1.0.2
Pydantic models for AWS Voice_id
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, voice_id

# boto3-pydantic-voice_id

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Voice_id.
pyvolar 0.0.1
Placeholder for the pyvolar python package, from the Vola Analytics Library. To obtain a copy of pyvolar, please contact Vola Dynamics LLC
Requires Python: >=3.8

# pyvolar python module

Copyright (c) 2025 Vola Dynamics LLC - All Rights Reserved

This package is a placeholder for the pyvolar python module. To obtain a copy of
pyvolar, please contact Vola Dynamics LLC.

https://voladynamics.com/

Note: if you are hosting a pypi server, and the server has begun defaulting to
pypi.org as it's source for pyvolar, the recommended steps are to either:

1. Constrain the version you're installing (eg, use `pyvolar>=3.20` when
   installing the package), or,
2. Disable fallback to pypi.org for your local package repository. For example,
   [`pypi-server`][pypi-server] allows disabling fallback with
   `--disable-fallback`.

[pypi-server]: https://pypi.org/project/pypiserver/
boto3-pydantic-verifiedpermissions 1.0.2
Pydantic models for AWS Verifiedpermissions
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, verifiedpermissions

# boto3-pydantic-verifiedpermissions

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Verifiedpermissions.
poslog 0.7
PosLog: A CRF-based Part-of-Speech Tagger for Log Messages
Requires Python: >=3.11

# PosLog
A CRF-based Part-of-Speech (POS) Tagger for Log Messages.

# Usage

## Use Default Model
There are three ways to use the default model:
1. Predict the PoS tags of a **list of tokens** returning a list of tags.
    ```python
    from poslog import PosLogTokenizer, PosLogCRF

    msg="Tag this sentence."

    tokenizer=PosLogTokenizer()
    tokens=tokenizer.tokenize(msg)
    # ['Tag', 'this', 'sentence', '.']

    pos_log=PosLogCRF()
    # predict(X:list[str])->list[str]
    pos_log.predict(tokens)
    # ['VERB' 'DET' 'NOUN' 'PUNCT']
    ```

2. Predict the PoS tags of a **string** returning a list of tags.
    ```python
    # predict_string(X:str)->list[str]
    pos_log.predict_string(msg)
    # ['VERB' 'DET' 'NOUN' 'PUNCT']
    ```

3. Predict the PoS tags of a **string** returning a list of tuples with token and tag.
    ```python
    # predict_string_as_tuple(X:str)->list[tuple[str,str]]
    pos_log.predict_string_as_tuple(msg)
    # [('Tag', 'VERB'), ('this', 'DET'), ('sentence', 'NOUN'), ('.', 'PUNCT')]
    ```


## Train Your Own Model
Define model name in constructor:
```python
pos_log=PosLogCRF(model_name="abs_path_to_my_model")
```
You can give `abs_path_to_my_model` as absolute path or relative path.  
Note: Relative paths models will stored in package directory `models/` and will be overwritten if you renew the environment.

PosLog takes training data as tokens and tags separately:
```python
train(X_train_tokens:list[list[str]], y_train_tags:list[list[str]])
```
Or as token and tag pairs:
```python
train_from_tagged_sents(tagged_sents:list[list[tuple[str,str]]])
```
After training, the model will be saved in the path you provided in the constructor.  
Note: Training will override existing model with the same name.

## Use Your Own Model
Just call the constructor with the model name:
```python
pos_log=PosLogCRF(model_name="my_model")
```


# Tokenization

Since PosLog was trained on a corpus we tokenized a specific way, we
included the tokenizer `PosLogTokenizer` in this package.

We use three preprocessing steps before tokenization to adapt to log
message specific characteristics:

1.  We escape *quotation marks* with spaces to distinguish them from
    words with tailing apostrophes.

2.  We extend NLTK's contraction list with 124 more cases where we split
    or replace contracted words.

3.  We apply NLTK's `word_tokenize` which makes a few more
    replacements and returns a token list.

The following shows an example of the three steps of
tokenization:

Example for the three steps of tokenization.
0 shows the input string and 3 the output list of tokenization.
```
0 (Input):  "Can't read 'block_x'."
1:          "Can't read 'block_x '."
2:          "Cannot read 'block_x '."
3 (Output): ["Can", "not", "read", "'", "block_x", "'", "."]
```




## Dependencies

PosLog relies on 
- `nltk` corpora: `words`, `stopwords`, `wordnet` and 
- `sklearn` for the CRF classifier `sklearn-crfsuite`.
boto3-pydantic-trustedadvisor 1.0.2
Pydantic models for AWS Trustedadvisor
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, trustedadvisor

# boto3-pydantic-trustedadvisor

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Trustedadvisor.
boto3-pydantic-translate 1.0.2
Pydantic models for AWS Translate
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, translate

# boto3-pydantic-translate

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Translate.
webcrawlerapi-langchain 0.1.1
LangChain integration for WebCrawlerAPI
Requires Python: >=3.8

# WebCrawlerAPI LangChain Integration

[WebcrawlerAPI](https://webcrawlerapi.com/) - is a website to LLM data API. It allows to convert websites and webpages markdown or cleaned content. 

**No subscription required**.

This package provides LangChain integration for [WebCrawlerAPI](https://webcrawlerapi.com/), allowing you to easily use web crawling capabilities with [LangChain](https://www.langchain.com/) document processing pipeline.

## Installation

Get your [API key](https://webcrawlerapi.com/docs/access-key) first

```bash
pip install webcrawlerapi-langchain
```

## Usage

### Basic Loading
```python
from webcrawlerapi_langchain import WebCrawlerAPILoader

# Initialize the loader
loader = WebCrawlerAPILoader(
    url="https://example.com",
    api_key="your-api-key",
    scrape_type="markdown",
    items_limit=10
)

# Load documents
documents = loader.load()

# Use documents in your LangChain pipeline
for doc in documents:
    print(doc.page_content[:100])
    print(doc.metadata)
```

### Async Loading
```python
# Async loading
documents = await loader.aload()
```

### Lazy Loading
```python
# Lazy loading
for doc in loader.lazy_load():
    print(doc.page_content[:100])
```

### Async Lazy Loading
```python
# Async lazy loading
async for doc in loader.alazy_load():
    print(doc.page_content[:100])
```

## Configuration

The loader accepts the following parameters:

- `url`: The URL to crawl
- `api_key`: Your WebCrawlerAPI API key
- `scrape_type`: Type of scraping (html, cleaned, markdown)
- `items_limit`: Maximum number of pages to crawl
- `whitelist_regexp`: Regex pattern for URL whitelist
- `blacklist_regexp`: Regex pattern for URL blacklist

### Links
- [WebCrawlerAPI Python SDK](https://github.com/WebCrawlerAPI/webcrawlerapi-python-sdk)
- [WebCrawlerAPI Documentation](https://webcrawlerapi.com/docs/getting-started)
- [WebCrawlerAPI API Reference](https://webcrawlerapi.com/docs/api-reference)

If you need help with integration feel free to [contact us](support@webcrawlerapi.com).

## License

MIT License