Agentic-Analyst

Key Features:

1. Plug and Play Streamlit UI:

   • An intuitive and interactive web interface powered by Streamlit that makes it easy to use and visualize data without extensive setup.

2. Agents with Data Science Speciality:

   • Data Visualization Agent: Generates a wide range of Plotly charts and visualizations.
   • Statistical Analytics Agent: Performs comprehensive statistical analyses and generates descriptive statistics.
   • Scikit-Learn Agent: Integrates with Scikit-Learn to build and evaluate machine learning models.
   • Preprocessing Agent: Handles data cleaning, transformation, and preparation tasks.

3. Completely Automated, LLM Agnostic:

   • The system operates with full automation and is agnostic to large language models (LLMs), making it adaptable to various AI models and technologies.

4. Built Using Lightweight Frameworks:

   • Constructed with efficient frameworks like DSPy, ensuring a lightweight and responsive application.

How to Run Locally

To run the Streamlit app locally, follow these steps:

1. Clone the Repository

First, clone the repository to your local machine using Git:

git clone respository_url_here

cd your-repository

2. Install Dependencies

Create a virtual environment and install the required Python packages. The required packages are listed in the requirements.txt file. Make sure you have pip installed, and then run:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

3. Set Up Environment Variables

You need to set up the OPENAI_API_KEY environment variable for the app to function. You can do this by adding the following line to your .env file or by exporting the variable in your terminal:

Using .env file:

Create a file named .env in the root of your project and add:

OPENAI_API_KEY=your_openai_api_key_here

Exporting in Terminal:

export OPENAI_API_KEY=your_openai_api_key_here

Replace your_openai_api_key_here with your actual OpenAI API key.

4. Run the Streamlit App

Start the Streamlit app using the following command:

streamlit run streamlit_app.py

Files in the System

The project consists of several key files, each serving a distinct purpose:

1. agents.py:

   • Description: Contains the definitions for various AI agents used in the system.
   • Key Agents:
     • agentic_analyst_ind: Routes queries to the appropriate agent based on user input and provides a detailed response.
     • agentic_analyst: Integrates a planner agent for routing queries and a code combiner agent for synthesizing outputs from multiple agents.
     • memory_summarize_agent: Summarizes agent responses and user queries.
     • error_memory_agent: Creates summaries of code errors and their corrections.

2. memory_agents.py:

   • Description: Defines agents that help summarize memory and errors.
   • Key Agents:
     • memory_summarize_agent: Provides summaries of agent responses and user goals.
     • error_memory_agent: Analyzes and summarizes code errors and suggested corrections.

3. retrievers.py:

   • Description: Contains functions and configurations for retrieving and processing data.
   • Key Functions:
     • return_vals: Collects useful information about data columns, such as statistics and top categories.
     • correct_num: Cleans numeric columns by removing commas and converting to float.
     • make_data: Pre-processes data and generates a description of the dataset.
   • Styling Instructions: Provides instructions for styling Plotly charts for different types of visualizations, including line charts, bar charts, histograms, pie charts, and heat maps.

4. new_frontend.py:

   • Description: The main Streamlit script that runs the application and integrates all the agents and functionalities provided in the other files.