semvec/pypi-packages huggingface dataset:
gothic 0.0.1
A pattern match->rewrite based programming language with austere & simple syntax/semantics. Like awk, but better.
Requires Python: >=3.11

# Gothic

A brief description of what your package does.

## Installation

```bash
pip install gothic
```

## Usage

```python
import mypackage

# Add usage examples
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
hi-humans 0.0.4
collection of biblical humans and their family relations
Requires Python: >=3.0

# hi_Humans Library
a Python collection of biblical humans and their family relations.
<br>
Please note, this is the source code of version 0.0.1, which is the starter file for my "Create Python Package" tutorial on YouTube:
<br>
https://youtu.be/9Ii34WheBOA

## Quickstart
the library provides different scopes of information

### Father Objects
a set of classes that represent biblical fathers, such as Abraham, Isaac and Jacob.

```
from hi_humans import hebrews

abraham = hebrews.Abraham()
print(abraham.__dict__)
```
### List of Humans
on a broader scope, the library traces different categories of humans, such as fathers, mothers and children.
```
from hi_humans import hebrews

heb_mothers = hebrews.mothers
print(heb_mothers)
```
bike4py 0.1.1
Bike4Mind API client
Requires Python: >=3.8
Keywords: api, bike4mind, bike4py, chat, client, llm

# bike4py

Bike4py is a Python client for the Bike4Mind API.

## Installation

```bash
pip install bike4py
```

## Usage

To use the client, you need to create a client with your refresh token:

```python
from bike4py import LLMClient

client = LLMClient(
    refresh_token="your_refresh_token"
)
```

Once you've created a client, you should connect to the websocket - this is
where responses to your prompt will be sent:

```python
# Connect to websocket, needed to see LLM responses
await client.connect()
```

You can then submit a prompt and stream the response.  The notebook ID can be found in the URL of the notebook you want to use.

```python
# Submit a prompt
request = ChatCompletionRequest(
  sessionId="%your_notebook_id%",
  message="Hello, how are you?"
)
response = client.submit_prompt(request)

# Stream the response
async for event in client.stream_events():
    if isinstance(event, StatusEvent):
        print(event.status)
    if isinstance(event, CompletionEvent):
        print(event.success)
    if isinstance(event, ContentEvent):
        print(event.content)
```

The event classes emitted by the `stream_events` method are:

- `StatusEvent`: Status updates from the LLM as Bike4Mind is processing the request.
- `ContentEvent`: While generating, the LLM will stream content in chunks; this event includes the accumulated progress of the response.
- `CompletionEvent`: At completion, this event is emitted with the final response.

The intent is that it's easy to separate the event types and process only the ones you need.  If you want streaming content for UI purposes, process the `ContentEvent` events.  If you want to know when the LLM has completed, process the `CompletionEvent` event.

## Upload a file

```python
file_id = client.upload_file("test.txt", "text/plain")
```

This will return a `file_id` which you can then pass to the `submit_prompt` method to include in a prompt.
```python
...
file_id = client.upload_file("test.txt", "text/plain")
request = ChatCompletionRequest(
  sessionId="%your_notebook_id%",
  message="Can you summarize this file for me?",
  fabFileIds=[file_id]
)
response = client.submit_prompt(request)
...
```