semvec/pypi-packages huggingface dataset:
synalinks-memory-cli 0.0.4
CLI for the Synalinks Memory API
Requires Python: >=3.10

# Synalinks Memory CLI

**Synalinks Memory** is the knowledge and context layer for AI agents. It lets your agents always have the right context at the right time. Unlike retrieval systems that compound LLM errors at every step, Synalinks uses **logical rules** to derive knowledge from your raw data. Every claim can be traced back to evidence, from raw data to insight, no more lies or hallucinations.

This CLI lets you interact with your Synalinks Memory directly from the terminal — add data, ask questions, and query your knowledge base.

## Installation

```bash
pip install synalinks-memory-cli
```

Or with [uv](https://docs.astral.sh/uv/):

```bash
uv add synalinks-memory-cli
```

Or run directly without installing using [uvx](https://docs.astral.sh/uv/concepts/tools/):

```bash
uvx synalinks-memory-cli list
uvx synalinks-memory-cli execute Users --format csv -o users.csv
```

## Usage

Set your API key:

```bash
export SYNALINKS_API_KEY="synalinks_..."
```

### Add a file

```bash
synalinks-memory-cli add data/sales.csv
synalinks-memory-cli add data/events.parquet --name Events --description "Event log" --overwrite
```

### Ask a question (while learning concepts and rules)

```bash
synalinks-memory-cli "What were the top 5 products by revenue last month?"
synalinks-memory-cli How are sales doing this quarter
```

### List predicates

```bash
synalinks-memory-cli list
```

### Execute a predicate

```bash
synalinks-memory-cli execute Users
synalinks-memory-cli execute Users --limit 50 --offset 10
```

### Search

```bash
synalinks-memory-cli search Users "alice"
```

### Export data as a file

```bash
synalinks-memory-cli execute Users --format csv
synalinks-memory-cli execute Users --format parquet -o users.parquet
synalinks-memory-cli execute Users -f json --limit 500
```

### Options

```
--api-key TEXT   API key (or set SYNALINKS_API_KEY)
--base-url TEXT  Override API base URL
--help           Show help message
```

## License

Apache 2.0
octopize.avatar 1.16.0
Python client for Octopize's avatar API
Requires Python: >=3.12

# Avatar Python Client

## Requirements

The `avatars` package requires Python 3.12 or above.

## Tutorials

The tutorials are available in [`notebooks/`](./notebooks) as jupyter notebooks.

To be able to run those, you must install the `avatars` package and also the dependencies in `requirements-tutorial.txt`.

We provide a helper script to setup these inside a virtual environments and run the notebooks.

Simply run the following command:

(NOTE: This assumes that you have the `just` software installed. This may not be the case for Windows by default.)

```bash
just notebook
```

If you don't have access to `just` or you want to setup the tutorial requirements manually, you can follow the installation section.

## Installation

### with pip

```bash
pip install octopize.avatar
```

### or, if you're using uv

```bash
uv add octopize.avatar
```

### or, if you're using poetry

```bash
poetry add octopize.avatar
```

## License

This software is made available through the Apache License 2.0.

## Contact

<help@octopize.io>

## Releasing

See internal docs.
g3visu 0.1.13
System G3 Visualization files' parsers and generators
Requires Python: >=3.11
Keywords: Elektroline, System G3

# Library Name

Collection of System G3 Visualization files' parsers and generators.

## Installation

The installation can be done with the package manager `pip`.

```bash
pip install g3visu
```

## Usage

Invoke from the terminal:

```bash
gen-visu --help
```

The package can also be used as a library:

```python
from g3visu import TypeInfo, SiteSVG
from g3visu.utils import SitesFileDownloader
```
valohai-yaml 0.55.0
Valohai.yaml validation and parsing
Requires Python: >=3.9
Keywords: strings, utility

# valohai-yaml

[![Build Status](https://github.com/valohai/valohai-yaml/actions/workflows/ci.yml/badge.svg)](https://github.com/valohai/valohai-yaml/actions/workflows/ci.yml)
[![Codecov](https://codecov.io/gh/valohai/valohai-yaml/branch/master/graph/badge.svg)](https://codecov.io/gh/valohai/valohai-yaml)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

Parses and validates `valohai.yaml` files.

Valohai YAML files are used to define how your machine learning project workloads and pipelines are ran on the [Valohai](https://valohai.com/) ecosystem. Refer to [Valohai Documentation](https://docs.valohai.com/) to learn how to write the actual YAML files and for more in-depth usage examples.

## Installation

```bash
pip install valohai-yaml
```

## Usage

### Validation

Programmatic usage:

```python
from valohai_yaml import validate, ValidationErrors

try:
    with open('path/to/valohai.yaml') as f:
        validate(f)
except ValidationErrors as errors:
    print('oh no!')
    for err in errors:
        print(err)
```

Command-line usage:

```bash
valohai-yaml my_yaml.yaml
echo $?  # 1 if errors, 0 if ok
```

### Parsing

```python
from valohai_yaml import parse

with open('path/to/valohai.yaml') as f:
    config = parse(f)

print(config.steps['cool step'].command)
```

# Development

```bash
# setup development dependencies
make dev

# run linting and type checks
make lint

# run tests
make test
```

## Snapshots

Update [syrupy](https://github.com/tophat/syrupy) snapshots after making changes to the examples.

```bash
pytest --snapshot-update
```