This project implements a full-stack web application using JavaScript RAG (Retrieval-Augmented Generation) and LlamaIndex. It allows users to interact with and retrieve information from various data sources through an interactive interface.
Leverages JavaScript RAG for intelligent query processing and response generation. Utilizes LlamaIndex for efficient data retrieval and indexing. Provides an interactive front-end for user interaction with the application.
Query engines act like translators between you and your data. They take your request (like a search or question) and translate it into a language the data source understands. Then, they grab the info, clean it up, and deliver it back to you in a user-friendly way. This makes it easier and faster to find what you need in databases, websites, or other data sources.
Imagine words like "king" and "queen" living close together in a special space. That's vector embeddings! Each data point (word, image, etc.) gets a unique position based on meaning, allowing machines to grasp connections between them. This helps with tasks like recommendations or understanding text.
This project is based on the course DeepLearning AI - JavaScript RAG Web Apps with LlamaIndex
- llamaindex
- Jerry Liu
- Logan Markewich
- Emanuel Ferreira
- Yi Ding
- DeepLearning.Al
- Diala Ezzeddine