Скачать или смотреть AI Data Assistant – RAG-Based Application with Hybrid Search | End-to-End GenAI Project

In this video, I showcase an AI Data Assistant built using Retrieval Augmented Generation (RAG) combined with Hybrid Search (Vector + Keyword) to deliver highly accurate, context-aware answers from large document collections.

This system allows users to upload files, store structured & unstructured data, and interact with it through a conversational AI interface—making it perfect for enterprise knowledge bases, research tools, and customer-support automation.

🔍 Key Features:

✅ Retrieval Augmented Generation (RAG) Pipeline
✅ Hybrid Search using Vector Embeddings + BM25
✅ Document Ingestion & Chunking
✅ Metadata-based Filtering
✅ FastAPI Backend with Streaming Responses
✅ SQLAlchemy + PostgreSQL / SQLite
✅ LLM Integration (OpenAI / Local Models)
✅ Conversation History Tracking
✅ Secure APIs & Scalable Architecture

🛠️ Tech Stack:

Python

FastAPI

LangChain

FAISS
React
Sentence Transformers
Hybrid Search Ranking Logic
BM25

📌 Use Cases:

📊 Enterprise Knowledge Assistant
📄 PDF & Document Q&A System
💼 Business Intelligence Chatbot
📚 Research Assistant
🤖 Internal Company Copilot

If you’re preparing for roles like GenAI Engineer, ML Engineer, or Data Scientist, this project demonstrates real-world production-grade architecture and advanced information-retrieval techniques.

👉 Don’t forget to Like 👍 | Share 🔄 | Subscribe 🔔 for more AI projects and tutorials!

#GenAI #RAG #HybridSearch #LLM #FastAPI #AIProjects #MachineLearning #DataScience #Python #VectorSearch #AIChatbot #OpenSource #ResumeProjects