PROJECT / CASE STUDY
A retrieval-augmented generation application designed to let users query documents using semantic search, embeddings, vector retrieval, and LLM-based response generation.
Role
AI Engineer / Developer
Focus
RAG, vector search, LLM workflows
Status
Built / Iterating
01 / USE CASE
The application allows users to ask questions over document content and receive context-aware answers. Instead of manually searching through long files, users can retrieve relevant information semantically and generate responses grounded in the uploaded knowledge base.
02 / TECH STACK
03 / ARCHITECTURE
Documents are loaded, processed, and split into chunks suitable for retrieval.
Chunks are converted into vector embeddings and stored inside a vector database for semantic retrieval.
User queries retrieve relevant chunks, which are passed into an LLM to generate grounded responses based on the uploaded data.