semvec/pypi-packages huggingface dataset:
flowise 1.0.4
Flowise SDK for Python to interact with the Flowise API.
Requires Python: >=3.7

# Flowise SDK - Python

The **Flowise SDK** for Python provides an easy way to interact with the Flowise API for creating predictions, supporting both streaming and non-streaming responses. This SDK allows users to create predictions with customizable options, including history, file uploads, and more.

## Features

- Support for streaming and non-streaming API responses
- Ability to include message history and file uploads

## Installation

You can install the SDK via pip:

```bash
pip install flowise
```

Upgrade version:

```bash
pip install --upgrade flowise
```

## Example

```py
from flowise import Flowise, PredictionData, IMessage, IFileUpload

def example_non_streaming():
    # Initialize Flowise client
    client = Flowise()

    # Create a prediction without streaming
    completion = client.create_prediction(
        PredictionData(
            chatflowId="abc",
            question="What is the capital of France?",
            streaming=False  # Non-streaming mode
        )
    )

    # Process and print the full response
    for response in completion:
        print("Non-streaming response:", response)

def example_streaming():
    # Initialize Flowise client
    client = Flowise()

    # Create a prediction with streaming enabled
    completion = client.create_prediction(
        PredictionData(
            chatflowId="abc",
            question="Tell me a joke!",
            streaming=True  # Enable streaming
        )
    )

    # Process and print each streamed chunk
    print("Streaming response:")
    for chunk in completion:
        print(chunk)


if __name__ == "__main__":
    # Run the non-streaming example
    example_non_streaming()

    # Run the streaming example
    example_streaming()
```

## Build & Publish

1. Increment version on `setup.py`
1. `pip install wheel`
2. `python setup.py sdist bdist_wheel`
3. `twine upload --skip-existing dist/*`
feynmagi 0.1.31
Feynmagi is a multi-modal LLM interactive agents solution.
Requires Python: >=3.6

<p align="center">
  <img src="feynmagi/static/img/Feynmagi_64x64.png" align="left" />
  <h1 align="center">Feynmagi V 0.1.31 </h1>
</p>


### Install ollama
- curl -fsSL https://ollama.com/install.sh | sh
- or go to : https://ollama.com/download

### Install feynmagi 
- pip install feynmagi 

### Or install from repo
- git clone https://github.com/Imag2020/feynmagi
- cd feynmagi
- pip install . 

### How to run
- feynmagi
- You can access it from any browser at localhost:5000

## 🌟 Feynmagi is a multi-modal tool for  MLLM interactive agents that integrates all the latest AI technologies into a single application, capable of running models locally on CPU or GPU (via Ollama). It incorporates proprietary code for embedding, RAG, and interactive AI, designed to model intelligent AIs with the goal of experimenting with AGI.
In this alpha test version 0.1.30, we add:
- Agents management with next agent call
- UI messages I/O display update with atatch button
  
In this alpha test version 0.1.30, we add:
- Agents management with live run and cron scheduler 
- swicth to gemma2 default local llm
- test tolls and fucntion calling with groq
- use of florence-2 for images
- send mail via smtp server (todo ==> use gmail OAuth)
- NB : mail config json under data dir, rename config_mail_to_do.json to config_mail.json
  
In this alpha test version 0.1.26, we have:
- Integration with OpenAI and Groq clients for external and faster inference, alongside local inference using Ollama.
- Chat capabilities with LLMs, images, documents, or AI agents.
- Ingestion of documents or web pages into a local vector database.
- Retrieval of past session histories.
- Support for new models in Ollama without code modification.
- use of florence-2 as vision model

The following agents are under development:
- Web search agent via Google 🌐
- Code interpreter agent ⚙️
- Memory agent 🧠

## ⚠️ Please note that this code is experimental and in the testing phase. It should not be tested on enterprise or confidential data. This code is shared for the AI community within an academic framework for scientific research, and the author does not authorize its use outside this scope without prior permission.

We welcome any contributions to the development towards this stated goal. 

### 
![Feynmagi 64x64](feynmagi/static/img/illustration_01.jpg)


Visit us at [feynmagi.com](http://feynmagi.com) or email us at [imed@feynmagi.com](mailto:imed@feynmagi.com).