pathwaycom/llm-app

Pathway AI Pipelines is a collection of ready-to-run templates for building AI-powered applications that can answer questions about your documents and data, always staying synchronized with the latest information. The core problem it addresses is that most AI assistants only know what they were trained on, or what you manually paste into them. This project lets you connect an AI directly to live data sources, such as Google Drive, SharePoint, Amazon S3, Kafka streams, or PostgreSQL databases, so that when those files or records change, the AI's knowledge updates automatically without you having to do anything. The templates use a technique called RAG, which stands for Retrieval-Augmented Generation. This means when you ask a question, the system first searches your documents for the most relevant passages, then passes those passages to a large language model (an AI like GPT) along with your question, so the answer is grounded in your actual data rather than guesswork. The underlying engine is the Pathway framework, which handles the live data synchronization and search using a built-in vector index, meaning you don't need to set up and maintain a separate database system like Pinecone or Redis. Applications run as Docker containers and expose an HTTP API, making them easy to deploy on any cloud or on your own server. The tech stack combines Python, Rust (inside the Pathway engine), and various large language model providers. You would use this when you want a private, automatically-updating AI assistant for company documents, contracts, financial reports, or any data that changes frequently and where outdated answers would cause real problems.