Description
AI/Python Developer Needed – Build a Local RAG Research Assistant We are looking for an experienced Python/AI Developer to build a local Retrieval-Augmented Generation (RAG) research assistant for a single user. This is a personal research tool, not an enterprise application. The objective is to create an AI assistant that can answer questions by searching a knowledge base of 300–500 research papers (PDFs) using semantic search and a Large Language Model (LLM). Every response should be based on retrieved documents and include citations to the original source. Scope of Work The selected freelancer will be responsible for the complete development of the application. 1. Research Paper Collection • Source and curate 300–500 publicly available research papers. • Preferred domain: Cybersecurity (network security, malware analysis, threat intelligence, cloud security, zero trust, vulnerability management, etc.). • If you believe another technical domain (AI, Machine Learning, Medical Research, etc.) offers a stronger public dataset, explain why in your proposal. 2. Document Ingestion Pipeline Develop an automated pipeline that can: • Import PDF files. • Extract text from PDFs. • Clean and preprocess text. • Split documents into chunks. • Generate embeddings. • Store embeddings in a vector database. • Allow additional PDFs to be added later without rebuilding the entire database. 3. Vector Database Use one of the following: • ChromaDB (preferred) • FAISS • Qdrant Explain your choice