The RAG-Powered PDF QA System explores how users can ask questions about PDF documents and receive responses grounded in retrieved document context. It brings together a retrieval workflow, vector search, and language-model inference.
02 / Approach
How it is put together.
01
Document question answering
The application centers on asking questions about uploaded PDF content instead of relying only on general model knowledge.
02
Retrieval
Qdrant is used as the vector-search component in the retrieval-augmented generation workflow.
03
Grounded generation
Retrieved context is combined with language-model generation to support document-grounded responses.
04
Local model exploration
LM Studio and Streamlit are part of the project stack for local inference and the application interface.
Project takeaway
What came out of it.
The prototype demonstrates a document-based Q&A workflow using retrieval and local model inference. Quantitative answer-quality or latency results are not specified in the current project brief.
