Show HN: RAG in 3 Lines of Python (pypi.org)

The newly released Python library, Piragi, introduces an innovative Retrieval Augmented Generation (RAG) interface that dramatically simplifies the integration of AI-driven document retrieval and processing. Users can set it up in just three lines of code to query various formats—such as PDFs, Markdown, and even audio—while benefiting from built-in features like a vector store, embeddings, smart citations, and auto-updates. This makes it a powerful tool for developers and researchers in the AI/ML community. Significantly, Piragi supports advanced retrieval techniques including HyDE for generating hypothetical document matches and hybrid searches combining traditional keyword and vector search methods. With its open-source approach, the library works seamlessly with local models and APIs, keeping query latency minimal thanks to its automatic background refresh capabilities. By enabling developers to create sophisticated pipelines effortlessly, Piragi paves the way for more efficient information retrieval and processing, enhancing both the utility and accessibility of AI technologies in various applications.