rag-services is an extensible platform that breaks down RAG pipelines into discrete microservices. It offers a document store service, a vector index service, an embedder service, multiple LLM inference services, and an orchestrator service to coordinate workflows. Each component exposes REST APIs, allowing you to mix and match databases and model providers. With Docker and Docker Compose support, you can deploy locally or in Kubernetes clusters. The framework enables scalable, fault-tolerant RAG solutions for chatbots, knowledge bases, and automated document Q&A.

SmartRAG is a modular Python library designed for retrieval-augmented generation (RAG) workflows with large language models. It combines document ingestion, vector indexing, and state-of-the-art LLM APIs to deliver accurate, context-rich responses. Users can import PDFs, text files, or web pages, index them using popular vector stores like FAISS or Chroma, and define custom prompt templates. SmartRAG orchestrates the retrieval, prompt assembly, and LLM inference, returning coherent answers grounded in source documents. By abstracting the complexity of RAG pipelines, it accelerates development of knowledge base Q&A systems, chatbots, and research assistants. Developers can extend connectors, swap LLM providers, and fine-tune retrieval strategies to fit specific knowledge domains.