DataTonic

A Data-Capable AGI-style Agent Builder of Agents , that creates swarms , runs commands and securely processes and creates datasets, databases, visualizations, and analyses.

• DataTonic solves simple tasks that require complex data processing
• it's perfect for data analytics and business intelligence

Use Case

DataTonic produces fixed business intelligence assets based on autonomous multimedia data processing.

• Sales Profiles
• Adaptive Summaries
• Dataset Analytics
• Research Reports
• Business Automation Applications

Based on those it can produce :

• Strategies
• Applications
• Analyses
• rich business intelligence

Business Case

DataTonic provides junior executives with an extremely effective solution for basic and time-consuming data processing, document creation or business intelligence tasks.

Enterprise Autonomation Agent

Do not wait for accounting, legal or business intelligence reporting with uncertain quality and long review cycles. DataTonic accelerates the slowest part of analysis : data processing and project planning execution.

How it works :

this section explains how Data Tonic works to produce what you need, consistently.

• your request is first processed according to a statement of work
• additional data is retrieved and stored
• multiple agents are created based on your specific use case

Technology :

this section describes how it works from a technical perspective:

• Autogen uses a semantic-kernel function calling agent to access the internet using the google api semantic-kernel then processes the new information and stores it inside a SQL database orchestrated by Taskweaver.
• Gemini is used in various configurations both for text using the autogen connector and for multimodal/image information processing.

How To Use

Please follow the instructions in this readme exactly.

Follow Tonic-AI

Star & Fork this repository

1. Step 1 : Star this repository
2. Step 2 : Fork this repository

Set Up Gemini

Get Google API key

Get Open AI Key(s)

Set Up Azure

Setup Instructions

This section provides instructions on setting up the project.

Step 1 : Clone the repository

clone this repository using the command line :

git clone https://github.com/DataTonic/DataTonic.git

Step 2: Configure DataTonic

Add Your Files to DataTonic

1. add relevant files one by one with no folder to the folder called 'src/autogen/add_your_files_here'
   • supported file types : ".pdf" , ".html" , ".eml" & ".xlsx":

Configuration

1. you'll need the keys you made above for the following.
2. use a text editor , and IDE or command line to edit the following documents.
3. Edit then save the files

OAI_CONFIG_LIST

edit 'OAI_CONFIG_LIST'

        "api_key": "your OpenAI Key goes here",

and

        "api_key": "your Google's GenAI Key goes here",

Configure OpenAI Key(s)

1. modify Line 135 in autogen_module.py
    ```python
    os.environ['OPENAI_API_KEY'] = 'Your key here'
    ```
2. modify .env.example
    ```os
    OPENAI_API_KEY = "your_key_here"
    ```
    save as '.env' - this should create a new file.
    **or** 
    rename to '.env' - this will rename the existing file.

3. modify src\tonicweaver\taskweaver_config.json
    ```json
    {
        "llm.api_base": "https://api.openai.com/v1",
        "llm.api_key": "",
        "llm.model": "gpt-4-1106-preview"
    }
    ```

4. 

Google API

edit ./src/semantic_kernel/semantic_kernel_module.py

line 64:    semantic_kernel_data_module = SemanticKernelDataModule('<google_api_key>', '<google_search_engine_id>')

and

line 158:    semantic_kernel_data_module = SemanticKernelDataModule('<google_api_key>', '<google_search_engine_id>')

with your google API key and Search Engine ID , made above.

src/semantic_kernel/googleconnector.py

Step 3: Install DataTonic

Install Taskweaver

from the project directory :

cd ./src/tonicweaver
git clone https://github.com/microsoft/TaskWeaver.git
cd ./src/tonicweaver/TaskWeaver
# install the requirements
pip install -r requirements.txt

Install DataTonic

from the project directory :

pip install -r requirements.txt

Step 4: Run the application

python run app.py