Answers HR policy questions from your own documents, retrieving relevant sections from a vector database to generate grounded responses with citations using OpenAI or Gemini.
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The HR Agent is a minimalist assistant designed to streamline access to human resources policies by answering questions directly from your organization's documents. It leverages Retrieval-Augmented Generation (RAG) to find the most relevant information within a vector database, then generates grounded, cited responses using OpenAI or Gemini. Engineered for speed, reliability, and trust, it offers a low-latency mode, conversation summary caching, and even functions in a basic retrieval-only mode if no LLM keys are configured, ensuring continuous operation and auditability through explicit citations.
Características Principales
01Low-latency mode for faster initial and follow-up responses
02Grounded answers with explicit citations (RAG)
03Conversation summary caching per session for reduced prompt context
04Multi-provider LLM support (OpenAI, Gemini, or Auto-fallback)
05Reliable retrieval-only operation when no LLM API keys are configured
060 GitHub stars
Casos de Uso
01Providing quick and cited answers to HR policy questions from internal documents
02Implementing a RAG-based HR assistant for employee self-service
03Demonstrating a robust, multi-LLM RAG system with fallback capabilities and performance optimizations