About This Project
Visual Generator Studio is an AI-based platform built to provide intelligent, context-aware responses from PDF documents. Leveraging Retrieval-Augmented Generation (RAG), the system allows users to upload PDFs, which are then processed and indexed for efficient semantic search. Using FAISS/ChromaDB, the platform performs vector-based retrieval to ensure highly accurate and contextually relevant answers. A Flask-based API handles user queries and delivers AI-generated responses, while PostgreSQL manages structured data, logs, and system metadata. This solution is designed for modularity, scalability, and high-performance query handling.
Technologies Used
Project Snapshot
- Status: Completed
- Duration: 180 Months
- Category: Media, Marketing
Project Team
Golakiya Vraj
Developer
Developed and deployed the RAG-based Visual Generator Studio, integrating Flask API, LangChain, FAISS/ChromaDB, and PostgreSQL. Designed pipelines for PDF ingestion and embeddings to enable intelligent information retrieval. Created semantic vector search workflows to improve contextual response accuracy. Built a Flask REST API to handle user queries and return AI-generated answers. Managed structured data storage with PostgreSQL for user queries, logs, and metadata. Optimized vector search for speed and performance, ensuring a modular, scalable, and maintainable backend architecture for AI-driven query processing.
YBAI SOLUTION BOT