# Nvidia Releases Auto Ontology for Agentic Data Context An open-source framework building semantic layers to improve LLM data reliability. - **Source:** [Hacker News: Front Page](https://brifing.si/source/b07aa772-b1a0-4a76-aa18-719c6f9e2dde) - **Published:** 2026-11-03 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/0a7cf6df-26c4-48af-9788-f31f50601a60 ## Bridging Agentic Analytics Gaps Nvidia introduced Auto Ontology to address the reliability issues inherent in agentic analytics. Agents often struggle with data context, leading to incorrect revenue metrics. This open-source library constructs a semantic layer from enterprise metadata, ensuring agents understand data origins, definitions, and relationships before executing queries against connected relational databases. ## Technical Architecture and Stack The framework utilizes a FastAPI backend with Python 3.12 and a Next.js 16 frontend. It leverages Postgres with pgvector for storing the catalog and vector embeddings. Authentication is handled via Better Auth, while HashiCorp Vault provides optional secure credential management for database connections, ensuring a robust enterprise-ready deployment. ## NVIDIA NIM Integration Auto Ontology relies on NVIDIA NIM endpoints for inference, supporting both hosted and self-hosted models. Users configure model roles through environment variables, allowing for flexible switching between reasoning and embedding models. The system defaults to the NVIDIA API catalog, requiring a valid nvapi key for chat and ingestion tasks. ## Deployment and Connectivity The project supports flexible deployment via Docker Compose for local stacks or Helm charts for Kubernetes clusters. It resolves database connections through either UI-added configurations or environment-defined strings. Once deployed, the system syncs database schemas, enabling natural language querying of structured data sources like Databricks, Snowflake, or Postgres. ## Conversation and Context Management FastAPI manages conversation history by loading up to five recent turns, bounded at 12,000 characters. It rewrites contextual queries into standalone questions before executing the text-to-SQL graph. This stateful approach ensures that follow-up questions remain accurate, while independent queries are processed without modification to maintain high performance and precision. ## MCP and Agent Interoperability The framework exposes data to agent harnesses like Cursor and Claude Desktop through an MCP server. By providing a standardized interface, it allows external agents to query enterprise data reliably. This integration complements existing semantic models, enabling a unified approach to data retrieval across various agentic workflows and tools. ## Security and Governance Security is managed through RBAC and API tokens for non-interactive scripts. The system requires a trusted internal service boundary, as the FastAPI layer relies on forwarded user headers. While the project is distributed under the Apache License 2.0, it explicitly excludes external contributions, maintaining a closed maintainer-only workflow for development. ## Strategic Role of Ontologies Auto Ontology represents a shift toward deterministic query generation in AI systems. By forcing agents to interact with a curated semantic layer rather than raw tables, it reduces hallucination risks. This framework serves as a critical infrastructure component for enterprises aiming to scale reliable, data-driven AI agents across complex environments. ## Sources covering this story - https://github.com/NVIDIA/auto-ontology — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._