semvec/pypi-packages huggingface dataset:
paraclient 1.37.1
Python client for Para
Requires Python: >=3.9

![Logo](https://s3-eu-west-1.amazonaws.com/org.paraio/para.png)

# Python Client for Para

[![NuGet version](https://badge.fury.io/py/paralient.svg)](https://badge.fury.io/py/paraclient)
[![Join the chat at https://gitter.im/Erudika/para](https://badges.gitter.im/Erudika/para.svg)](https://gitter.im/Erudika/para)

## What is this?

**Para** was designed as a simple and modular backend framework for object persistence and retrieval.
It helps you build applications faster by taking care of the backend. It works on three levels -
objects are stored in a NoSQL data store or any old relational database, then automatically indexed
by a search engine and finally, cached.

This is the Python client for Para.

### Quick start

**Prerequisites:**

- [uv](https://github.com/astral-sh/uv)
- Python 3.9+

1. Use the [PyPI](https://pypi.python.org/pypi) package manager to install the Python client for Para:

```sh
$ pip3 install paraclient
```

2. Initialize the client with your access and secret API keys:

```python
from paraclient import ParaClient

paraclient = ParaClient('ACCESS_KEY', 'SECRET_KEY')
paraclient.setEndpoint("http://localhost:8080")
```

## Documentation

### [Read the Docs](https://paraio.org/docs)

## Development

This project uses [uv](https://github.com/astral-sh/uv) for dependency management, builds, and publishing, and targets Python 3.9+.

1. Install uv by following the [official instructions](https://docs.astral.sh/uv/getting-started/installation/).
2. Run `uv sync` first. This installs every dependency declared in `pyproject.toml` and pinned in `uv.lock` into `.venv/`.
3. Run the test suite via `uv sync --extra test` and `uv run python -m unittest`.
4. Build distributable artifacts with `uv build` (output located in `dist/`).

When dependencies change, update the `[project]` section of `pyproject.toml`, then regenerate the lock file with `uv lock --upgrade`.

The test suite uses [Testcontainers](https://testcontainers.com/) to spin up Para Docker container automatically,
so ensure Docker is installed and the daemon is running before invoking `uv run python -m unittest`.
You can override some environment variables (see `tests/test_paraclient.py`).

## Contributing

1. Fork this repository and clone the fork to your machine
2. Create a branch (`git checkout -b my-new-feature`)
3. Implement a new feature or fix a bug and add some tests
4. Commit your changes (`git commit -am 'Added a new feature'`)
5. Push the branch to **your fork** on GitHub (`git push origin my-new-feature`)
6. Create new Pull Request from your fork

For more information see [CONTRIBUTING.md](https://github.com/Erudika/para/blob/master/CONTRIBUTING.md)

## License
[Apache 2.0](LICENSE)
experimental-intelligence 1.0.0
Üst düzey fizik, kuantum ve matematik simülasyon motoru.
Requires Python: >=3.8
Keywords: science, quantum, physics, astrophysics, simulation

# EI Experimental Intelligence
Bu kütüphane; astrofizik, kuantum mekaniği ve ileri matematik hesaplamaları için geliştirilmiştir.
hexa-ddd-blueprint 0.1.1
CLI tool that scaffolds opinionated Python projects following DDD + Hexagonal Architecture
Requires Python: >=3.14

# hexa-ddd-blueprint

[![CI](https://github.com/AymanKastali/hexa-ddd-blueprint/actions/workflows/ci.yml/badge.svg)](https://github.com/AymanKastali/hexa-ddd-blueprint/actions/workflows/ci.yml)
[![PyPI](https://img.shields.io/pypi/v/hexa-ddd-blueprint)](https://pypi.org/project/hexa-ddd-blueprint/)

CLI tool that scaffolds opinionated Python projects following **DDD + Hexagonal (Ports & Adapters) Architecture**.

## Installation

```bash
uv tool install hexa-ddd-blueprint
```

## Usage

### Interactive mode (default)

```bash
hexa-ddd-blueprint new
```

Launches prompts for project name, description, author, DB choice, and Python version.

### Flag-driven mode

```bash
hexa-ddd-blueprint new myproject \
  --description "My awesome service" \
  --author "John Doe" \
  --db postgres \
  --python 3.14 \
  --no-docker \
  --no-ci
```

### All flags

| Flag | Default | Description |
|---|---|---|
| `[NAME]` | (prompted) | Project name |
| `--description` / `-d` | (prompted) | Project description |
| `--author` / `-a` | (prompted) | Author name |
| `--db` | (prompted) | Database: `postgres`, `none` |
| `--python` | `3.14` | Python version |
| `--no-docker` | false | Skip Docker/Compose |
| `--no-ci` | false | Skip GitHub Actions CI |
| `--no-devcontainer` | false | Skip devcontainer |
| `--no-interactive` / `-y` | false | Use defaults, skip prompts |

## Generated Project Structure

```
myproject/
├── src/myproject/
│   ├── domain/           # Pure business logic
│   │   └── shared/       # Shared kernel (base entity)
│   ├── application/      # Use cases, ports, DTOs
│   └── adapters/
│       ├── config/           # Pydantic Settings
│       ├── inbound/
│       │   └── api/
│       │       └── rest/     # FastAPI REST adapter
│       └── outbound/
│           ├── persistence/
│           │   └── postgres/ # DB-specific adapter
│           └── logging/          # Logging adapter
├── tests/
├── docs/architecture/    # PlantUML diagrams
├── docker/
├── .github/workflows/
└── .devcontainer/
```

## Development

```bash
uv sync
uv run pytest
```
cognee-community-tasks-scrapegraph 0.1.0
Package containing custom cognee tasks for scraping web content using ScrapeGraphAI
Requires Python: <=3.13,>=3.11

# cognee-community-tasks-scrapegraph

Custom cognee tasks for scraping web content using [ScrapeGraphAI](https://github.com/ScrapeGraphAI/scrapegraph-py).

## Overview

This package provides two async tasks:

- **`scrape_urls`** – scrape a list of URLs with a natural language prompt and return structured results.
- **`scrape_and_add`** – scrape URLs and ingest the content directly into a cognee dataset.

## Installation

```bash
uv pip install cognee-community-tasks-scrapegraph
```

Or install locally with all dependencies:

```bash
cd packages/task/scrapegraph_tasks
uv sync --all-extras
# OR
poetry install
```

## Requirements

You need two API keys:

| Variable | Description |
|---|---|
| `LLM_API_KEY` | OpenAI (or other LLM provider) API key used by cognee |
| `SGAI_API_KEY` | [ScrapeGraphAI](https://scrapegraphai.com) API key |

Set them in your environment or in a `.env` file:

```bash
export LLM_API_KEY="sk-..."
export SGAI_API_KEY="sgai-..."
```

## Usage

### Scrape only

```python
import asyncio
from cognee_community_tasks_scrapegraph import scrape_urls

results = asyncio.run(
    scrape_urls(
        urls=["https://cognee.ai", "https://docs.cognee.ai"],
        user_prompt="Extract the main content, title, and key information from this page",
    )
)

for item in results:
    print(item["url"], item["content"])
```

### Scrape and add to cognee

```python
import asyncio
from cognee_community_tasks_scrapegraph import scrape_and_add

asyncio.run(
    scrape_and_add(
        urls=["https://cognee.ai"],
        user_prompt="Extract the main content and key information",
        dataset_name="web_scrape",
    )
)
```

## Run the example

```bash
cd packages/task/scrapegraph_tasks
uv run python examples/example.py
# OR
poetry run python examples/example.py
```

## API Reference

### `scrape_urls`

```python
async def scrape_urls(
    urls: List[str],
    user_prompt: str = "Extract the main content, title, and key information from this page",
    api_key: Optional[str] = None,
) -> List[dict]
```

Returns a list of dicts:

```python
[
    {"url": "https://example.com", "content": {...}},           # success
    {"url": "https://bad.invalid", "content": "", "error": "..."}, # failure
]
```

### `scrape_and_add`

```python
async def scrape_and_add(
    urls: List[str],
    user_prompt: str = "Extract the main content, title, and key information from this page",
    api_key: Optional[str] = None,
    dataset_name: str = "scrapegraph",
) -> Any
```

Scrapes all URLs, combines the successful results into a single text document, calls `cognee.add`, and then `cognee.cognify`. Returns the cognify result.