file-to-vector

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This app allows users to upload and store various file types into their own Supabase vector database locally.

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# File-to-Vector

> **Note**: This repository is preserved as a technical showcase. Built in early 2025, it provides a simple GUI for uploading documents to Supabase pgvector, functionality now better served by LangChain, LlamaIndex, or native platform features.

## Description

File-to-Vector is a lightweight, user-friendly application designed to streamline the process of converting documents into vector embeddings for storage in a Supabase database. It enables researchers and developers to build semantic search and retrieval-augmented generation (RAG) pipelines without writing complex data processing code.

The tool supports a variety of file types including PDFs, DOCX, and spreadsheets, leveraging state-of-the-art embedding models from Cohere and OpenAI to generate high-quality vector representations.

![Demo](https://github.com/user-attachments/assets/4a230635-bf3f-4d62-b812-5dc4e8ff6120)

## Key Features

* **Multi-Format Support:** Seamlessly extracts text from PDFs (including tables via pdfplumber), Word documents, and CSV files.
* **Dual Embedding Providers:** Choose between Cohere's `embed-english-v3.0` or OpenAI's `text-embedding-3-small` models based on your use case.
* **Intelligent Chunking:** Automatically splits documents into semantically meaningful chunks using NLTK sentence tokenization.
* **Real-Time Monitoring:** Track upload progress with live progress bars and success notifications.
* **Direct Supabase Integration:** Vectors are stored directly into your Supabase `pgvector`-enabled database for immediate use in RAG applications.

## Technical Architecture

The system is built on a modular Python stack optimized for simplicity and extensibility:

* **Streamlit:** Provides an interactive, multi-page frontend for file uploads, database monitoring, and configuration.
* **PyMuPDF & pdfplumber:** Handle PDF text and table extraction with high fidelity.
* **python-docx:** Parses Word documents to extract paragraph text.
* **Cohere / OpenAI SDKs:** Generate semantic embeddings via API calls.
* **Supabase Python Client:** Manages authentication and vector storage operations against a PostgreSQL database with the `pgvector` extension.

## Application Workflow

File-to-Vector follows a straightforward pipeline from document ingestion to vector storage:

### 1. Phase: Document Upload

Users upload files through the Streamlit interface. The application detects the file type and routes it to the appropriate text extraction module.

### 2. Phase: Text Extraction & Chunking

Text is extracted from the uploaded document using specialized parsers. The content is then split into chunks (default: 300 characters) using sentence-aware tokenization to preserve semantic coherence.

### 3. Phase: Embedding Generation

Each text chunk is sent to the selected embedding provider (Cohere or OpenAI). The resulting vectors are padded or truncated to match the expected database dimension.

### 4. Phase: Vector Storage

The embeddings, along with the original text and metadata, are inserted into the configured Supabase table. Progress is displayed in real-time, and users receive confirmation upon completion.

## Installation & Setup

```bash
# Clone the repository
git clone https://github.com/jackvandervall/file-to-vector.git

# Install dependencies (Recommend using a virtual environment)
pip install -r requirements.txt

# Navigate to the application directory
cd app

# Configure Environment Variables
# Create a .env file or use the Upload tab to enter your Supabase URL, service_role key, and embedding API keys

# Launch the Application
streamlit run main.py
```

## Credits

Developed by Jack van der Vall during an internship at the Erasmus Centre for Data Analytics.

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