🥳 File2Vector - NEW RELEASE

A tool that allows users to upload and store various file types in their own Supabase vector database.
Extract text from DOCX, PDFs, and spreadsheets, generating embeddings while storing them efficiently.

✨ Features

✔️ Supports multiple file types: DOCX, PDFs, CSVs, and more
✔️ Automatic text extraction from documents
✔️ Embeddings generation for vector storage
✔️ Seamless integration with Supabase
✔️ User-friendly interface for easy file uploads

📌 How to Install

Follow these steps to install and run File2Vector on your local machine.

🔹 Step 1: Install Dependencies

Before running the app, install the required Python packages.

pip install -r requirements.txt

If there is no requirements.txt, you can install the dependencies manually:

pip install streamlit

(Add any additional dependencies if necessary.)

🔹 Step 2: Navigate to the App Directory

Move into the app/ directory:

cd app

🔹 Step 3: Run the Streamlit App

Start the application by running:

streamlit run main.py

This will launch the File2Vector web app in your default browser.

📌 How to Use

1. Set up Supabase

   • Go to Supabase
   • Navigate to Project Settings > Data API
   • Copy your Project URL and service_role key
   • Paste them into the Upload tab of File2Vector

2. Upload Files

   • Select the documents you want to convert into embeddings
   • The tool will automatically process and store them in your vector database

3. Provide Feedback

   • Use the contact page to share your experience or report issues

🚀 Roadmap 2025

• API support for any Embedding provider
• Upload to any Vector Database
• Instant RAG functionality using your own LLM API
• More to be announced...

🔗 Connect with Me

💼 LinkedIn: Jack van der Vall
📂 GitHub: jackvandervall