06-Literature informed object detection (using RAG)

In this video we build a fully local RAG (Retrieval-Augmented Generation) pipeline that lets you ask questions about a collection of scientific papers and get grounded, cited answers - all running on your own machine with no API keys and no internet connection required after setup.

We use this pipeline in the context of automated object detection in scientific images, specifically glomerulus detection in kidney histology. Before training any model, a researcher needs to make three decisions: which text phrases should describe the target object, what confidence thresholds are appropriate, and how many annotated images are sufficient. Instead of guessing, we ask the published literature directly.

What we cover:
• What RAG is and how it works: indexing, embedding, retrieval, and synthesis
• Parsing scientific PDFs with PyMuPDF
• Converting text chunks to 384-dimensional embedding vectors with all-MiniLM-L6-v2
• Storing and querying the vector index with ChromaDB using cosine similarity
• Running Llama 3.2 locally via Ollama for answer synthesis — no cloud, no API
• Connecting RAG outputs directly to Grounding DINO phrase selection in the annotation tool
• Building the full pipeline as a PyQt5 desktop GUI consistent with the rest of the series

This approach works for any scientific domain and any collection of PDFs, not just kidney histology. If you have a corpus of papers and want to query them intelligently, this is the pipeline to build.

Code: https://github.com/bnsreenu/LLM-Assisted-Scientific-Image-Annotation-Tool

#Python #RAG #LLM #MachineLearning #ComputerVision #ScientificImaging #DigitalPathology #Ollama #Llama #ChromaDB #GroundingDINO #DigitalSreeni

Видео 06-Literature informed object detection (using RAG) канала DigitalSreeni