semvec/pypi-packages huggingface dataset:
aa-rag 0.4.3
RAG server for ai2apps.
Requires Python: >=3.10

# aa-rag

## Description

RAG Server for [AI2APPS](https://github.com/Avdpro/ai2apps). This server provides a Retrieval-Augmented Generation (RAG)
API to support advanced AI applications.

---

## Requirements

1. **OpenAI API Key:**
    - The service supports only the OpenAI interface style.
   - Ensure your `.env` file includes the following line:
     ```
     OPENAI_API_KEY=<your_openai_api_key>
     ```

2. **Environment Setup:**
   - Make sure your environment is properly configured to load environment variables from a `.env` file.
   - For complete details on how to configure the application using environment variables and a `.env` file, please see
     the [Configuration Parameters](CONFIGURATION.md) document.

---

## Installation

### Installation via PyPI

Install the package from PyPI:

```bash
pip install aa-rag
```


### Installation via Source Code

The project build via the `uv` tool. To install the project from source code, follow these steps:

1. **Install uv:**
    - On macOS, it is recommended to use Homebrew:
      ```bash
      brew install uv
      ```
    - For other operating systems, please refer to
      the [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/#pypi).

2. **Clone the Repository:**
   ```bash
   git clone https://github.com/continue-ai-company/aa_rag.git
   cd aa_rag
   ```

3. **Synchronize Dependencies:**
    - Install dependencies as specified in the `uv.lock` file:
      ```bash
      uv sync
      ```
    - This command will create the virtual environment in the current project directory and install all necessary
      dependencies.


### Optional
If you want to install the package with the `online` extra, you can use the following command:
```bash
pip install "aa-rag[online]" # install package with pip
uv sync --extras online # install package with uv
```

---

## Usage

1. **Start the Web Server:**
    - If you installed the package via PyPI, you can run the server using the following command:
      ```bash
      aarag
      ``` 
    - If you installed the package from source code and are using the `uv` tool, you can run the server using the following command:
      ```bash
      uv run aarag
      ```
      
2. **Access the API Documentation:**
    - Open your browser and navigate to:
     ```
     http://localhost:222/docs
     ```
   - This page provides an interactive Swagger UI to explore and test all available APIs.

---
biomeai 1.0.0
A package for easy integration with NVIDIA AI capabilities
Requires Python: >=3.6

# BiomeAI

Simple Python package for Multiple Sequence Alignment search using NVIDIA's ColabFold.

## Install
```bash
pip install biomeai
```

## Quick Usage

```python
from biomeai import ColabFoldMSA

# Initialize with API key
msa = ColabFoldMSA(api_key="your-api-key")
# Or set NVCF_RUN_KEY environment variable

# Your protein sequence
sequence = "MVPSAGQLALFALGIVLAACQALENSTSPLSADPPVAAAVVSHFNDCPDSHTQFCFHGTCRFL"

# 1. Use a single database
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302"]
)

# 2. Use multiple databases
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302", "PDB70_220313"]
)

# 3. Use all databases (Cascaded Search)
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302", "PDB70_220313", "colabfold_envdb_202108"]
)

# Save results (optional)
msa.save_results(results, "results.json")
```

## Available Databases

- `Uniref30_2302`: Universal Reference Database
- `PDB70_220313`: Protein Data Bank
- `colabfold_envdb_202108`: Environmental Database

## Additional Options

```python
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302"],
    e_value=0.0001,        # E-value threshold
    iterations=1,          # Number of iterations
    output_formats=["a3m", "fasta"]  # Output formats
)
```

## Async Support

```python
import asyncio

async def search_msa():
    results = await msa.search(
        sequence=sequence,
        databases=["Uniref30_2302"]
    )
    return results

results = asyncio.run(search_msa())
```

## Development

Install with development dependencies:
```bash
pip install biomeai[dev]
```

Run tests:
```bash
pytest
```

## License

MIT License - see the [LICENSE](LICENSE) file for details.
django-s3files 0.0.4.2
Django AWS S3 File Manager enables you to manage files directly from the Django admin interface, allowing you to read, upload, and delete files without directly accessing AWS.
Requires Python: >=3
Keywords: django, aws, s3, media, file, manager, django-storage, django-admin

# Django AWS S3 File Manager (django-s3files)

## Description

Django AWS S3 File Manager enables you to manage files directly from the Django admin interface, allowing you to read,
upload, and delete files without directly accessing AWS.  
It seamlessly integrates with [django-storages](https://github.com/jschneier/django-storages.git).

**Documents**
- PyPI https://pypi.org/project/django-s3files/
- Github https://github.com/runbykim/django-s3files

## Installation

Installing from PyPI is as easy as doing:
```bash
pip install django-s3files
```

## Settings
1. Add `django-s3files` to your `INSTALLED_APPS` in `settings.py`.

2. Add include url above your admin url. You can change the url as you wish.
    ```python
    urlpatterns = [
        path('admin/s3files/', include('s3files.urls')),
        path('admin/', admin.site.urls),
    ]
    ```

## Authentication Settings
- `AWS_ACCESS_KEY_ID` (required)
- `AWS_SECRET_ACCESS_KEY` (required)
- `AWS_S3_REGION_NAME` (required)
- `AWS_STORAGE_BUCKET_NAME` (required)
- `AWS_STORAGE_BASE_DIR` (str: '')
- `PER_PAGE` (int: 25)
- `SHOW_THUMBNAILS` (bool: False)

## URL

Your file manager URL will be the `include` URL defined in step 2 of the settings, followed by `/browse`.  
For example: `https://example.com/admin/s3files/browse`.

## Dependencies

The package works with any version of Python 3, tested with Django==4.2, and requires boto3==1.37.38.
Other versions have not been tested.

## Contributing

1. Check for open issues at [the project issue page](https://github.com/runbykim/django-s3files/issues) or open a new issue 
to start a discussion about a feature or bug.
2. Fork the [django-storages repository on GitHub](https://github.com/runbykim/django-s3files.git) to start making changes.
3. Make sure the bug is fixed or the feature is implemented correctly.
4. Wait for me to merge the pull request.
Android-tcpdump-tool 1.0.1
A command-line tool for capturing Android app traffic using tcpdump.
Requires Python: >=3.6

# Android_tcpdump_tool

`Android_tcpdump_tool` 是一个用于抓取 Android 应用流量的命令行工具。

## 安装

```bash
pip install Android_tcpdump_tool
```

## 使用方法
- -a 或 --apk: 目标应用的 APK 文件路径。
- -t 或 --timeout: 抓包超时时间（秒），默认为 300 秒。
- -o 或 --output: 流量文件存储位置

## 示例

```bash
Android_tcpdump_tool -n com.example.app -t 600
```