ResilientRAG

ResilientRAG is a production-ready Retrieval-Augmented Generation (RAG) system designed to deliver reliable and scalable AI-powered document question answering. The system allows users to upload documents (such as PDFs), which are processed into semantic embeddings and stored in a vector database for efficient retrieval. When a user submits a query, the system retrieves the most relevant document chunks and augments the prompt before sending it to the LLM for response generation.

Unlike traditional RAG implementations that directly depend on a single LLM provider, ResilientRAG integrates a fault-tolerant LLM orchestration layer powered by ResilientLLM. This layer ensures stability through adaptive retries, exponential backoff, token-aware rate limiting, circuit breakers, and multi-provider fallback mechanisms. In case of API failures, rate limits, network instability, or provider overload, the system automatically recovers without disrupting the user experience.

By combining intelligent document retrieval with resilient generation, the project demonstrates real-world AI system design principles focused on reliability, scalability, and high availability. ResilientRAG emphasizes not just intelligent responses, but production-grade AI infrastructure capable of operating under unpredictable conditions.