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Educational platforms today face several challenges that hinder effective learning:
These pain points are amplified when handling documents with sensitive or encoded data, leading to incomplete information extraction and unreliable learning aids.
The UEI Project provides a cutting-edge solution by combining OCR techniques, vector-based document embeddings, secure backend infrastructure, and scalable processing to deliver a reliable, scalable, and secure AI educational assistant. Key features include:
Securely stores uploaded documents in AWS S3 and logs relevant metadata into PostgreSQL.
Efficiently extracts encoded text from PDFs using Tesseract OCR.
Generates document embeddings with JINA, stored in PGVector for retrieval.
Handles query processing, document retrieval, and contextual answer generation using LLMs.
Ensures the accuracy of AI-generated answers by detecting hallucinations and following fallback mechanisms.
Handles simultaneous user interactions through Celery and Redis task queuing.
Ensures encrypted communication via SSL through Nginx.
Supports a wide range of Indian languages for OCR through Tesseract.
Python Core logic and backend implementation.
FastAPI development for file handling and query processing.
PostgreSQL with PGVector - Storing document embeddings and metadata efficiently.
JINA Embeddings v3 Generating vector representations of text documents.
AWS S3 - Secure storage for uploaded documents.
Tesseract - Extracting text from documents, including encoded text.
OLLAMA (Llama3.1:70b), OpenAI GPT - Context-aware response generation and fallback.
Nginx and Gunicorn - Secure communication between frontend and backend.
Redis and Celery - Managing parallel task processing and task queuing.
LangChain and LangGraph - Document retrieval and context management.
The UEI Project is hosted on DBMART, an efficient GPU-based server platform for low-cost AI model inference and processing. The backend communicates securely through Nginx and Gunicorn. SSL encryption via GoDaddy ensures secure interaction between the frontend and backend.