This repository provides a powerful solution for building AI-enriched spreadsheets with real-time web access. The application combines tavily's advanced search capabilities with to transform your business spreadsheets with intelligent web-sourced information.

With this application, you can:

• 📊 Enrich spreadsheet cells with AI-generated content backed by live web data
• 🧠 Entity extraction and unstrcutured data processing with LLMs
• 🔄 Process entire columns as a batch for efficient data enhancement
• 📑 Source citations for all web-sourced information
• 📂 Export your enriched data as CSV files for further use

Designed for ease of customization, you can extend this core implementation to:

• Integrate proprietary data sources (e.g., vectorDB, GraphDB)
• Modify the LangGraph agent architecture
• Configure different LLMs
• Perform time-range or domain-filtered web search using tavily's advanced search parameters
• Perform news or finance specialty search through tavily's topic parameter)

This application requires API keys from the following services:

• tavily API (default) or Gemini optionally
• OpenAI

a. Create a .env file in the project's root directory with your API keys:

TAVILY_API_KEY=<your API key>
OPENAI_API_KEY=<your API key>
GEMINI_API_KEY=<your API key>
VITE_WS_URL=ws://localhost:8000
VITE_APP_URL=http://localhost:5173

b. Create a .env.development file in the ui directory with:

VITE_API_URL=http://localhost:8000
VITE_WS_URL=ws://localhost:8000

1. Create a virtual environment and activate it:

python3.11 -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

2. Install dependencies:

python3.11 -m pip install -r requirements.txt

3. From the root of the project, run the backend server:

python app.py

1. Alternatively, build and run the backend using Docker from the root of the project:

# Build the Docker image
docker build -t spreadsheet .

# Run the container
docker run -p 8000:8000 --env-file .env spreadsheet

1. Navigate to the frontend directory:

cd ui

2. Install dependencies:

npm install

3. Start the development server:

npm run dev

4. Launch the app by pasting http://localhost:5173/ in your browser

This repository includes everything required to create a functional AI-powered spreadsheet with web access:

The core backend logic, powered by tavily, LLMs, and LangGraph:

• graph.py – Defines the agent architecture, state management, and processing nodes.

Interactive React frontend for dynamic user interactions and spreadsheet responses.

• app.py – FastAPI server that handles API endpoint.

Feel free to submit issues, PRs, and enhancement requests!

Have questions, feedback, or looking to build a custom solution? We'd love to hear from you!

• Email our team directly:
  • Dean Sacoransky
  • Michael Griff

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