semvec/pypi-packages huggingface dataset:
poma 0.3.6
Official Python SDK for the Poma document-processing API
Requires Python: >=3.10
Keywords: chunking, structure, rag, poma, documents, ai

![POMA AI Logo](https://raw.githubusercontent.com/poma-ai/.github/main/assets/POMA_AI_Logo_Pink.svg)
# 📚 POMA: Preserving Optimal Markdown Architecture

## 🚀Quick-Start Guide

### Installation

Requires Python 3.10+. Install the core packages:
```bash
pip install poma
```

For different integrations:
```bash
pip install 'poma[langchain]'
pip install 'poma[llamaindex]'
pip install 'poma[qdrant]'

# Or LangChain/LlamaIndex/Qdrant including example extras:
pip install 'poma[all]'
```


- You may also want: `pip install python-dotenv` to load API keys from a .env file.
- API keys required (POMA_API_KEY) for the POMA AI client via environment variables.
- **To request a POMA_API_KEY, please contact us at sdk@poma-ai.com**


### Example Implementations — all examples, integrations, and additional information can be found in our GitHub repository: [poma-ai/poma](https://github.com/poma-ai/)

We provide example implementations to help you get started with POMA AI:
- example.py — A standalone implementation for documents, showing the basic POMA AI workflow with simple keyword-based retrieval
- example_langchain.py — Integration with LangChain, demonstrating how easy it is to use POMA AI with LangChain
- example_llamaindex.py — Integration with LlamaIndex, showing how simple it is to use POMA AI with LlamaIndex
-

*Note: The integration examples use OpenAI embeddings. Make sure to set your OPENAI_API_KEY environment variable, or replace the embeddings with your preferred ones.*


All examples follow the same two-phase process (ingest → retrieve) but demonstrate different integration options for your RAG pipeline.

! Please do NOT send any sensitive and/or personal information to POMA AI endpoints without having a signed contract & DPA !
DAWpy 2026.2.20.0
A Digital Audio Workstation in your CLI.
Requires Python: >=3.12

# DAWpy

Surprisingly nobody took the name DAWpy and I took this as a golden opportunity to finally learn to compose music.

I tried many of the popular Digital Audio Workstation software and none of them made sense to me, so I decided to write this package to see if I can finally make some music.
crewai-humanod 0.1.0
CrewAI integration for delegating tasks to real humans via the Humanod API.
Requires Python: >=3.8

# CrewAI Humanod Integration

Empower your [CrewAI](https://crewai.com) agents to interact with the physical world by hiring real humans through the [Humanod API](https://humanod.app).

## Installation

```bash
pip install crewai-humanod
```

## Quick Start

Give any agent in your crew the ability to hire a human.

```python
from crewai import Agent, Task, Crew
from crewai_humanod import HumanDelegationTool

# Initialize the tool
humanod_tool = HumanDelegationTool(api_key="your_humanod_api_key")

# Create an agent powered by this tool
manager = Agent(
    role="Operations Manager",
    goal="Ensure physical goods are inspected properly before shipping.",
    backstory="You manage quality control. You can hire humans to perform physical inspections.",
    tools=[humanod_tool],
    verbose=True,
    allow_delegation=False
)

inspecion_task = Task(
    description="Hire a human to go inspect the factory in Berlin and take 3 photos.",
    expected_output="Confirmation that a human has been hired.",
    agent=manager
)

crew = Crew(
    agents=[manager],
    tasks=[inspecion_task]
)

result = crew.kickoff()
print(result)
```
langchain-humanod 0.1.0
LangChain integration for delegating tasks to real humans via the Humanod API.
Requires Python: >=3.8

# LangChain Humanod Toolkit

Allows LangChain agents to securely interact with the physical world by hiring real humans through the [Humanod API](https://humanod.app).

## Installation

```bash
pip install langchain-humanod
```

## Quick Start (with LangGraph or traditional Agents)

```python
from langchain_humanod import HumanodToolkit
from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(temperature=0)

# Instantiate the Toolkit
toolkit = HumanodToolkit(api_key="your_humanod_api_key")
tools = toolkit.get_tools()

# Create agent
agent = initialize_agent(
    tools, 
    llm, 
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, 
    verbose=True
)

agent.run("Can you hire a human to go take a photo of the Eiffel Tower right now? Offer 15 EUR.")
```

## Included Tools
- `hire_human`: Creates a task request and deploys it to the human workforce.
- `check_human_task_status`: Verifies the status of a deployed physical task.
humanod 0.1.0
The official Python SDK for Humanod: Hire humans on-demand via API.
Requires Python: >=3.8

# Humanod Python SDK

The official Python client for [Humanod](https://humanod.app) – the API that allows AI Agents and developers to seamlessly hire humans for physical tasks in the real world.

## Installation

```bash
pip install humanod
```

## Quick Start
Delegate a physical task to a human with just a few lines of code.

```python
from humanod import HumanodClient, CreateTaskRequest, TaskCategory

client = HumanodClient(api_key="your_api_key_here")

# 1. Define the mission
task_spec = CreateTaskRequest(
    title="Take a photo of the Eiffel Tower",
    description="I need a vertical photo of the Eiffel Tower right now for an article.",
    price=15.0,  # EUR
    category=TaskCategory.MEDIA,
    location_name="Paris, France",
    deliverables="1 clear high-res photo",
    validation_criteria="The tower must be centered and fully visible.",
    skills_required=["photography"]
)

# 2. Deploy to the real world
task = client.hire_human(task_spec)
print(f"Mission deployed! View task ID: {task.task_id}")
```

## Core Methods
- `hire_human(task_spec)`: Creates a new task.
- `check_task_status(task_id)`: Gets the current status of the task.
- `get_submissions(task_id)`: Retrieves all proofs of work submitted by humans.
- `review_submission(application_id, validation)`: Approve or reject work.
- `cancel_task(task_id)`: Cancels the mission and refunds the budget.

## Standardized Agent-to-Human Schema
This SDK enforces the open `"Agent-to-Human"` standard JSON schema via statically typed **Pydantic** models (e.g., `CreateTaskRequest`, `ValidationRequest`).
seed-farmer 8.0.2
The AWS Professional Services CLI tool SeedFarmer for GitOps support
Requires Python: <3.14,>=3.10
Keywords: AWS, GitOps

# Seed-Farmer

[![PyPi](https://img.shields.io/pypi/v/seed-farmer)](https://pypi.org/project/seed-farmer/)
[![Python Version](https://img.shields.io/pypi/pyversions/seed-farmer.svg)](https://pypi.org/project/seed-farmer/)
[![License](https://img.shields.io/pypi/l/seed-farmer)](https://github.com/awslabs/seed-farmer/blob/main/LICENSE)

Seed-Farmer (seedfarmer) is an opensource orchestration tool modeled after [GitOps deployments](https://www.gitops.tech/). It has a CommandLine Interface (CLI) and is based in Python.

Please see our [SeedFarmer Documentation](https://seed-farmer.readthedocs.io/en/latest/).

For information related to the architecture of Seed-Farmer or the deployment scheme ... please see:

- [Seed-Farmer multi-account architecture](https://seed-farmer.readthedocs.io/en/latest/concepts/architecture/)
- [Seed-Farmer Concepts](https://seed-farmer.readthedocs.io/en/latest/concepts/)

Seed-Farmer uses modular code deployments ([see modules](https://seed-farmer.readthedocs.io/en/latest/guides/module-development/)) leveraging [manifests](https://seed-farmer.readthedocs.io/en/latest/reference/manifests/) and [deployspecs](https://seed-farmer.readthedocs.io/en/latest/reference/deployspec/), keeping track of changes and applying changes as need / detected.

## Getting Started

The Seed-Farmer library is available on PyPi.  Install the library in a python virtual environment.

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install seed-farmer
```

Seed-Farmer also supports [uv](https://docs.astral.sh/uv/guides/tools/), and can be installed as a tool or into a virtual environment.

```bash
uv tool install seed-farmer
```

A [project](https://seed-farmer.readthedocs.io/en/latest/guides/project-development/) is necessary to begin creating modules.  

## Development

To get started with developing you need to setup a virtual environment and install the required python dependencies.

Setup virtual environment and install dependencies:

```bash
make install
```

Run unit tests

```bash
make test
```

Validate ruff formatting and mypy

```bash
make validate
```

Fix ruff formatting issues

```bash
make format
```