Building a Local Semantic Search Engine Over Thousands of Academic PDFs

Whether you are an academic researcher, student, or patent analyst, searching through a folder containing thousands of PDFs using basic keyword search is frustrating. Synonym mismatches mean that searching for "neuromorphic" might miss documents mentioning "synaptic circuits." The solution is Local Semantic Search.

This tutorial outlines how to construct a fast, fully offline semantic search index that runs locally on your standard laptop, maintaining absolute privacy and costing nothing.

Step 1: Local Text Chunking

First, we use a lightweight Python library (such as PyPDF) or local command-line OCR tools to extract clean text from your PDF folder. We then divide the text into logical, overlapping chunks (e.g., 500 characters with a 100-character overlap) to preserve local context and ensure that sentences aren’t cut in half.

Step 2: Vector Embedding Generation

Next, we load a small, high-performance sentence embedding model (like all-MiniLM-L6-v2) via Hugging Face’s Transformers library. Running locally, this model converts each text chunk into a 384-dimensional vector, representing its semantic meaning. We index these vectors inside a local vector database like SQLite-VSS, Chroma, or a simple NumPy array, enabling sub-millisecond semantic queries.