Verbis Graph Engine – GraphRAG Knowledge Retrieval

Verbis Graph Engine is a container-based GraphRAG (graph-enhanced retrieval augmented generation) knowledge retrieval engine designed for developers and enterprises evaluating advanced AI-driven document understanding workflows. Unlike traditional vector-only RAG solutions, Verbis Graph Engine combines semantic vector embeddings with structured knowledge graph traversal. This hybrid approach enables multi-document reasoning, entity-aware retrieval, and more accurate responses for complex queries that span multiple sources. A free demo container is available to support functional testing, experimentation, and technical evaluation before adopting professional or enterprise editions. The free demo provides a fully functional evaluation environment, delivered as a standard container image compatible with Microsoft Azure container services. It is intended for functional testing, experimentation, and technical evaluation only. Key capabilities include: - GraphRAG-based retrieval combining vector similarity and knowledge graph relationships - Multi-document reasoning across uploaded content - Integrated FastAPI back end with OpenAPI (Swagger) documentation available at /docs, accessible via a dedicated tab in the Streamlit web interface - Web-based Streamlit interface for document management and chat-based querying - Health endpoint for orchestration and monitoring at /_stcore/health Deployment and usage notes: - The application runs as a single-container instance and is not designed for production or multi-tenant workloads - Data is retained at the application level during the lifetime of the container and user account; persistence across container redeployment is not guaranteed - No license keys, subscriptions, or in-app purchases are required for the free demo - Azure infrastructure costs apply based on the selected deployment configuration. This solution is designed for developers, solution architects, and AI teams building and operating graph-based knowledge retrieval systems on their own documents across evaluation, pilot, and production environments.