semvec/pypi-packages huggingface dataset:
vortex-fusion 0.0.2
Paper - Pytorch
License: MIT
Requires Python: <4.0,>=3.10
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# Vortex Fusion
This is the first ever implementation of a joint Transformer + Mamba + LSTM architecture. The flow is the following: `mamba -> transformer -> lstm` in a loop. Perhaps with more iteration on model design, we can find a better architecture but this architecture is the future.


## install

```bash
$ pip3 install -U vortex-fusion

```

## Usage
```python
import torch
from vortex_fusion import VortexFusion

# Generate random input tensor
x = torch.randint(0, 10000, (1, 10))

# Create an instance of the VortexFusion model with dimension 512
model = VortexFusion(dim=512)

# Pass the input tensor through the model to get the output
output = model(x)

# Print the shape of the output tensor
print(output.shape)
```

# License
MIT


# Citation
Please cite Swarms in your paper or your project if you found it beneficial in any way! Appreciate you.

```bibtex
@misc{swarms,
  author = {Gomez, Kye},
  title = {{Swarms: The Multi-Agent Collaboration Framework}},
  howpublished = {\url{https://github.com/kyegomez/swarms}},
  year = {2023},
  note = {Accessed: Date}
}
```
document-insighter 0.0.11
Document Insighter Python Client
License: MIT


# Document Insighter Client

Document Insighter Client is a Python library for interacting with the Document Insighter API. It allows you to query document extractions and process the data for various document types.

## Getting Started

### Prerequisites

- Python 3.7+

### Installation

1. Install the required dependencies using the provided `requirements.txt`:

    ```bash
    pip install -U document-insighter
    ```

### Getting Started

The following steps demonstrate how to use the Document Insighter Client in your Python project:


#### Okta Based Authentication

- Include the necessary environment variables in your project or by setting them in your environment. The required environment variables are:
    - `INSIGHTER_CLIENT_IDP`
    - `INSIGHTER_CLIENT_ID`
    - `INSIGHTER_CLIENT_SECRET`
    - `INSIGHTER_CLIENT_TOKEN_PATH`
    - `INSIGHTER_TENANT`

- Now, let's proceed to the next step. Install the required packages for the example script.
```bash
pip install pandas==2.2.2
```

- Once the packages are installed, you can run the example to fetch and print out document extractions within the specified date range.
```bash
python example/query_extractions_okta.py --category NB_COA --start_date 2024-05-01 --end_date 2024-07-10 --page_size 50 --tags HB_Ops tag1 tag2
```