Hoya26/backend

Ethix Backend

A Flask-based API server for the Ethix greenwashing detection platform. This backend provides AI-powered analysis of products and companies to identify misleading environmental claims.

Technology Stack

Component	Technology
Framework	Flask
AI/LLM	Google Gemini, Ollama
Vector Database	ChromaDB
Document Store	MongoDB
Embeddings	Ollama (nomic-embed-text)
Vision AI	Ollama (ministral-3)
Computer Vision	OpenCV, Ultralytics (YOLO)
Document Processing	PyPDF, openpyxl, pandas

Prerequisites

• Python 3.10+
• MongoDB instance
• Access to ChromaDB server
• Access to Ollama server
• Google API Key (for Gemini)

Environment Variables

Create a .env file in the backend directory:

GOOGLE_API_KEY=your_google_api_key
MONGO_URI=your_mongodb_connection_string
CHROMA_HOST=http://your-chromadb-host
OLLAMA_HOST=https://your-ollama-host

Variable	Description	Default
GOOGLE_API_KEY	Google Gemini API key	(required)
MONGO_URI	MongoDB connection string	(required)
CHROMA_HOST	ChromaDB server URL	http://chroma.sirblob.co
OLLAMA_HOST	Ollama server URL	https://ollama.sirblob.co

Installation

1. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

Running the Server

Development

python app.py

The server will start on http://localhost:5000.

Production

gunicorn -w 4 -b 0.0.0.0:5000 app:app

API Endpoints

Gemini AI

Method	Endpoint	Description
POST	/api/gemini/ask	Chat with AI using RAG context
POST	/api/gemini/rag	Query with category filtering
POST	/api/gemini/vision	Vision analysis (not implemented)

Incidents

Method	Endpoint	Description
POST	/api/incidents/submit	Submit a greenwashing report
GET	/api/incidents/list	Get all confirmed incidents
GET	/api/incidents/<id>	Get specific incident details

Reports

Method	Endpoint	Description
GET	/api/reports/	List all company reports
POST	/api/reports/search	Semantic search for reports
GET	/api/reports/view/<filename>	Download a report file

RAG

Method	Endpoint	Description
POST	/api/rag/ingest	Ingest document chunks
POST	/api/rag/search	Search vector database

External Services

The backend integrates with the following external services:

Service	URL	Purpose
ChromaDB	http://chroma.sirblob.co	Vector storage and similarity search
Ollama	https://ollama.sirblob.co	Embeddings and vision analysis

Docker

Build and run using Docker:

docker build -t ethix-backend .
docker run -p 5000:5000 --env-file .env ethix-backend

Or use Docker Compose from the project root:

docker-compose up backend

Core Features

Greenwashing Detection

The incident submission pipeline:

1. User uploads product image or company PDF
2. Vision model detects brand logos (for products)
3. PDF text extraction (for company reports)
4. Embedding generation for semantic search
5. RAG context retrieval from ChromaDB
6. Gemini analysis with structured output
7. Results stored in MongoDB and ChromaDB

RAG (Retrieval-Augmented Generation)

• Supports CSV, PDF, TXT, and XLSX file ingestion
• Documents are chunked and batched for embedding
• Prevents duplicate ingestion of processed files
• Semantic search using cosine similarity