semvec/pypi-packages huggingface dataset:
stpyvista 0.2.1
Streamlit component to render PyVista 3D visualizations
Requires Python: >=3.10
Keywords: 3D, panel, pyvista, streamlit, visualization

# 🧊 `stpyvista`

### Show PyVista visualizations in Streamlit

This component takes a PyVista plotter object and shows it on Streamlit as an interactive element (as in it can be zoomed in/out, moved and rotated, but the visualization state is not returned). It uses [Panel](https://panel.holoviz.org/reference/panes/VTK.html#working-with-pyvista) to render PyVista plotter objects within an iframe.

****

### 📦 Installation

```sh
pip install stpyvista
```
****
### 📚 Demo and documentation 

[https://stpyvista.streamlit.app](https://stpyvista.streamlit.app)

****
### 🛠️ Source code

[https://github.com/edsaac/stpyvista](https://github.com/edsaac/stpyvista)
vektori 0.1.0
AI Memory that Actually Works — Graph-based memory for LLM applications
Requires Python: >=3.8
Keywords: ai, graph, llm, memory, rag, vector

# Vektori Python SDK

> AI Memory that Actually Works — Graph-based memory for LLM applications.

## Installation

```bash
pip install vektori
```

## Quick Start

```python
from vektori import Vektori

# Cloud
memory = Vektori(api_key="vk_your_key")

# Self-hosted
memory = Vektori(url="http://localhost:8080")

# Store memories
memory.ingest(
    user_id="user_123",
    messages=[
        {"role": "user", "content": "I love pizza"},
        {"role": "assistant", "content": "Noted!"}
    ]
)

# Retrieve context
result = memory.retrieve(user_id="user_123", query="favorite food")
print(result.context)  # "User loves pizza"
```

## Documentation

Full documentation: [https://docs.vektori.cloud](https://docs.vektori.cloud)

## License

MIT
flatland 1.0.0
HTML form management and validation
Requires Python: >=3.10
Keywords: schema, validation, data, web, form, forms, roundtrip

flatland
========

Flatland maps between rich, structured Python application data and the
string-oriented flat namespace of web forms, key/value stores, text
files and user input.  Flatland provides a schema-driven mapping
toolkit with optional data validation.

Flatland is great for:

  - Collecting, validating, re-displaying and processing HTML form
    data

  - Dealing with rich structures (lists, dicts, lists of dicts, etc.)
    in web data

  - Validating JSON, YAML, and other structured formats

  - Associating arbitrary Python types with JSON, .ini, or sys.argv
    members that would otherwise deserialize as simple strings.

  - Reusing a single data schema for HTML, JSON apis, RPC, ...

The core of the flatland toolkit is a flexible and extensible
declarative schema system representing many data types and structures.

A validation system and library of schema-aware validators is also
provided, with rich i18n capabilities for use in HTML, network APIs
and other environments where user-facing messaging is required.

Links
-----

Docs: https://flatland.readthedocs.io/

Repo / issue tracking: https://github.com/discorporate/flatland

PyPI: https://pypi.org/project/flatland/