semvec/pypi-packages huggingface dataset:
web-base 0.1.5
Base para manipulação web com selenium.
Requires Python: >=3.8

# Web Base

> Esse material passa por atualizações periódicas. Você pode acompanhar as notas de alterações [aqui](./docs/changelog.md).


![PyPI - Version](https://img.shields.io/badge/pypi-0.1.0-orange)
![Python](https://img.shields.io/badge/language-Python-blue?logo=python)
![Cobertura de Testes](https://img.shields.io/badge/cobertura-80%25-brightgreen?logo=pytest)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/fastapi.svg?color=%2334D058)](https://pypi.org/project/fastapi)

# Descrição

Metodos úteis para configuração e utilização do selenium de forma simplificada.
rndc-python 0.1.1
Python client for ISC BIND's RNDC
Requires Python: >=3.10
Keywords: bind, dns, named, rndc, tsig

# rndc-python

Python client library for talking to ISC BIND's RNDC service.

## Requirements

- Python 3.10+

## Installation

```bash
pip install rndc-python
```

Or using uv:

```bash
uv add rndc-python
```

### Command-line Interface

The package includes a CLI tool `rndc-python-cli`:

```bash
# Using CLI options
rndc-python-cli -s 127.0.0.1 -p 953 -a sha256 -k <base64-secret> status

# Using environment variables
export ZPAPI_RNDC_HOST=127.0.0.1
export ZPAPI_RNDC_PORT=953
export ZPAPI_RNDC_ALGORITHM=sha256
export ZPAPI_RNDC_SECRET=<base64-secret>
rndc-python-cli status

# Mix of both (CLI options override env vars)
rndc-python-cli --port 954 reload
```

## Configuration

The client can read its settings from environment variables (or a `.env` file):

- `ZPAPI_RNDC_HOST`
- `ZPAPI_RNDC_PORT`
- `ZPAPI_RNDC_ALGORITHM` (e.g. `hmac-sha256`)
- `ZPAPI_RNDC_SECRET`
- `ZPAPI_RNDC_TIMEOUT`
- `ZPAPI_RNDC_MAX_RETRIES`
- `ZPAPI_RNDC_RETRY_DELAY`

You can also configure the client directly in Python:

```python
from rndc_python import RNDCClient, TSIGAlgorithm

client = RNDCClient(
    host="127.0.0.1",
    port=953,
    algorithm=TSIGAlgorithm.SHA256,
    secret="your-base64-secret-here",
    timeout=10,
    max_retries=3,
    retry_delay=2,
)
```

All parameters are optional if you have configured environment variables or a `.env` file.

## Usage

### Python API

```python
from rndc_python import RNDCClient

with RNDCClient() as rndc_client:
    print(rndc_client.call("status"))
```

## Development

See [DEVELOPMENT.md](DEVELOPMENT.md) for development setup, building, and testing instructions.
llama-index-tools-praisonai 0.1.0
LlamaIndex tool integration for PraisonAI multi-agent framework
Requires Python: >=3.9
Keywords: llamaindex, llama-index, praisonai, ai-agents, multi-agent, llm, tools

# llama-index-tools-praisonai

LlamaIndex tool integration for [PraisonAI](https://github.com/MervinPraison/PraisonAI) multi-agent framework.

## Installation

```bash
pip install llama-index-tools-praisonai
```

## Prerequisites

1. Install and start PraisonAI server:
```bash
pip install praisonai
praisonai serve agents.yaml --port 8080
```

## Usage

### Basic Usage with LlamaIndex Agent

```python
from llama_index_tools_praisonai import PraisonAIToolSpec
from llama_index.agent.openai import OpenAIAgent

# Create the PraisonAI tool spec
spec = PraisonAIToolSpec(api_url="http://localhost:8080")
tools = spec.to_tool_list()

# Create agent with PraisonAI tools
agent = OpenAIAgent.from_tools(tools, verbose=True)

# Use the agent
response = agent.chat("Research the latest trends in AI and summarize them")
print(response)
```

### Using Individual Tools

```python
from llama_index_tools_praisonai import PraisonAIToolSpec

spec = PraisonAIToolSpec()

# Run a specific agent
result = spec.run_agent(query="Research quantum computing", agent="researcher")
print(result)

# Run the full workflow
result = spec.run_workflow(query="Create a blog post about AI")
print(result)

# List available agents
agents = spec.list_agents()
print(agents)
```

### Async Usage

```python
import asyncio
from llama_index_tools_praisonai import PraisonAIToolSpec

async def main():
    spec = PraisonAIToolSpec()
    
    # Async agent execution
    result = await spec.arun_agent(query="Research AI", agent="researcher")
    print(result)
    
    # Async workflow execution
    result = await spec.arun_workflow(query="Create content")
    print(result)

asyncio.run(main())
```

### With ReAct Agent

```python
from llama_index_tools_praisonai import PraisonAIToolSpec
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI

spec = PraisonAIToolSpec()
tools = spec.to_tool_list()

llm = OpenAI(model="gpt-4o-mini")
agent = ReActAgent.from_tools(tools, llm=llm, verbose=True)

response = agent.chat("Use PraisonAI to research and write about machine learning")
print(response)
```

## Configuration

| Parameter | Default | Description |
|-----------|---------|-------------|
| `api_url` | `http://localhost:8080` | PraisonAI server URL |
| `timeout` | `300` | Request timeout in seconds |

## Available Tools

| Tool | Description |
|------|-------------|
| `run_agent` | Run a specific PraisonAI agent (e.g., researcher, writer) |
| `run_workflow` | Run the full multi-agent workflow |
| `list_agents` | List all available agents |

## Links

- [PraisonAI Documentation](https://docs.praison.ai)
- [PraisonAI GitHub](https://github.com/MervinPraison/PraisonAI)
- [LlamaIndex Documentation](https://docs.llamaindex.ai)

## License

MIT