Builds cost-free RAG systems using parallel document processing and local vector embeddings.
I3 is a specialized RAG (Retrieval-Augmented Generation) construction tool designed for systematic reviews and large-scale document analysis. It automates the entire pipeline from downloading PDFs via Open Access sources to text extraction, token-based chunking, and vector database creation using ChromaDB. By leveraging local embedding models like all-MiniLM-L6-v2, I3 enables researchers and developers to build high-performance search systems at zero cost. It includes a robust parallel processing engine that can handle hundreds of documents efficiently, making it the ideal choice for creating searchable knowledge bases from academic papers or private document sets.
Key Features
010 GitHub stars
02Built-in error handling and checkpoint protocols for systematic reviews
03Automated PDF downloading from arXiv, Semantic Scholar, and OpenAlex
04Zero-cost local embeddings and vector database creation using ChromaDB
05High-throughput parallel document processing for large-scale collections
06Precise token-based chunking with tiktoken for optimized retrieval
Use Cases
01Creating local research knowledge bases from academic paper collections
02Building cost-effective RAG applications for sensitive or offline data
03Automating the PDF retrieval and indexing stage of systematic reviews
