DatasheetRAG is a Retrieval-Augmented Generation system for embedded engineers that enables intelligent querying of hardware datasheets. It retrieves relevant sections using vector search and generates context-aware answers, helping users quickly understand registers, protocols, and configurations without manual search.
Problem Statement Microcontroller datasheets are: 1.Large and complex 2.Difficult for beginners to understand 3.Contain dense technical information Traditional search methods fail to provide contextual explanations.
Solution
This project implements a Traditional RAG pipeline that: Parses datasheet PDFs Splits content into chunks Generates embeddings Retrieves relevant sections Uses an LLM to answer queries
Architecture
User Query --> Retriever (FAISS) --> Relevant Datasheet Chunks --? LLM (FLAN-T5 Large) -->Generated Answer
Tech Stack Python, LangChain, FAISS (Vector Database), HuggingFace Transformers, FLAN-T5 Large (LLM), Sentence Transformers (Embeddings), Google Colab.