semvec/pypi-packages huggingface dataset:
atomict 1.2.25
The client application for the https://atomictessellator.com/
License: MIT
Requires Python: >=3.10
Keywords: atomic, tessellator, cli, client, fhi-aims, kpoint, sqs

# Atomic Tessellator - CLI package

## Installation
```
pip install atomict

# ", with a git url
pip install git+https://github.com/AtomicTessellator/atomic_cli#egg=atomict

# Troubleshooting
If pip install is not working, try:
pip install --upgrade setuptools pip wheel
```

## Installation for devs
```
# installs linting and formatting tools with some general defaults
pip install -e ".[dev]"
```

Enable verbose logging for debugging:

```
export AT_DEBUG=enabled
```

# CLI Usage

## Get a list of available commands

```$ tess```

![Alt text](img/tess.png?raw=true "tess")

You can get help for each command with `tess <command> --help`. This will print the command-specific help and options.
## Log in and store authentication token

```$ tess login```

This will prompt you for your username and password.

```$ tess token```

This command prints out your current token.


## Get a list of available projects

```$ tess project get```

![Alt text](img/at_project_get.png?raw=true "tess project get")

## Get a list of tasks

```$ tess task get```

![Alt text](img/at_task_get.png?raw=true "tess task get")

## Get tasks with a specific status

```$ tess task get --status completed```

![Alt text](img/at_task_get_completed.png?raw=true "tess task get --status completed")

## Configuration

Tab completion is available. Run the hidden command:

```tess completion```

This will print out the instructions for enabling tab completion for your shell.
ragpy 0.1.0
Unified RAG service abstraction for vector stores like Qdrant.
License: MIT
Requires Python: >=3.9

## RAGpy

### Usage

Create a vectorstore client (currently only Qdrant is supported).
Set QDRANT_API_KEY environment variable first.

```
from ragpy.vectorstore import QdrantService
from ragpy.embeddings import OpenAIEmbeddingService

embedder = OpenAIEmbeddingService(api_key="your-openai-key")
qdrant = QdrantService("https://your-qdrant-server-url", embedder=embedder)
```

Create collection:

```
qdrant.create_collection('collection-name')
```

Upload chunks of data to a collection:

```
chunks = ['list of', 'text chunks']
qdrant.upsert('collection-name', chunks)
```

Query collection:

```
query_points = qdrant.search('collection-name', 'your query')
for point in query_points:
    print(f'score: {point["score"]}')
    print(f'text: {point["text"]}')
```