NVIDIA拥有发布 Auto Ontology，是Apache 2.0许可下的开放库。 NVIDIA Auto Ontology是一个语义层框架，从企业元数据中构建上下文层。 Agents使用该层来对照认证的商业定义来写出SQL. 寄存器明确陈述了问题：如果数字错误，快速收入答案就毫无意义。
TL; DR 数据
- 库：默认LLM为nvidia/nemotron-3-nano-30b-a3b，并有Nemotron嵌入和重排模型。
- 运行在：Docker 编译一个本地堆栈，或通过Helm的Kubernetes. 推论使用托管的NVIDIA NIM端点或自托管的NIM.
- 性能：工程力强，公共证据薄。
- 最佳：在NVIDIA测试(自报)中，肿瘤创造时间缩短了90%。
- 最差：0个已公布的文本对SQL精度分数。 eval回放船装了个吊带，没有结果。
- 底线：一个开放的，MCP已准备好的上下文层，任何agent的取用都可以查询。 但你必须自己运行基准测试才能相信它。
NVIDIA自动肿瘤学是什么？
它是位于agent和数据仓之间的上下文层。 它告诉了agent数据在哪里生活，以及字段的含义。 也用称是否使用一个字段，如何使用，以及它来自何处。
这里有五个工作：
- 发现：从元数据生成本体学，并连接现有的语义模型。
- 依据：为度量衡、实体和关系赋予agents信任的含义。
- 验证：对照经认证的定义和分界线检查产生的查询。
- 博览会：通过MCP,CLI和API服务上下文。
- 学习：将认证的校正折回本体学。
NVIDIA的开发者将输出设定为开始的本体。 商业专家仍考核，完善并认证。
自动肿瘤学是怎么运作的？
该堆有4个核心服务。 A Next.js 16 前端处理网络UI和端口3000上的认证。 FastAPI后端有LangChain运行聊天，NL到SQL以及端口3001上的目录API. 一个摄取工人写作嵌入了港口3002. 后用pgvector存储目录，肿瘤和向量。
源数据库可以是Databricks,Postgres,Snowflake或DuckDB. 模型是通过终点、关键和模型的环境“三重”设定的。 在推理，非理性，嵌入和再排位方面分别存在不同的角色。 任何OpenAI相容的终点工程。
MCP服务器暴露什么？.
MCP服务器docs描述9个只读工具。 询问问题返回答案，SQL 和行作为单独的字段。 100行的结果上限。 check rready确认一个已编译的语义层，并存在活的数据库连接。
没有浏览原始计划的工具。 NVIDIA是故意选择的，因此agents通过语义层到达数据。 基础OpenAPI spec发布87个操作。 签名通过 OAuth 运行，每一次通话都带有签名用户权限。
如何确定基准？
NVIDIA 单独搭载一具自发自上自上自上自上自上自下自上自下自上自下自上自下自上自下自上自下自上自下自上自下自上自下自上自下自上自下自上自下自上自下自上自上自上自上自上自上自上自下自上自上自上自上自下自上自上自上自上自下自上自上自上自上自上自上自上自上自上自上自上自下自上自上自上自下自上自下自上自上自下自下自上自上自上自上自下自下自上自下自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上自上它在"NeMo Gym"中运行了2个臂：一个只有"Schema"的LLM控制器和地表基底的agent. 斯克林是决定性的行刑比赛，没有LLM裁判。
捆绑数据集包括BIRD Dev(1,534个问题，11个数据库)和Mini-Dev (500个问题). FDA Bench-Lite 新增 169问题，而BEAVER默认为100个问题样本。
取回新增 2限制可选的几发样本和价值锚只去肿瘤臂。 用它们来衡量的三角洲覆盖了这些投入，而不只是地面。 它经过测试的配置也通过NVIDIA推断网关上的Claude模型来引导双臂。
它与其他语义层有什么比较？
部署和可用性
本地设置为docker 组成 - d -- -- 在填入.env后建立。 Kubernetes 用户可以得到Helm图表和DEPLAYMENT.md指南。 NVIDIA团队还将后端发布为NGC容器。 NVIDIA API 从构建键。 nvidia.com覆盖了默认模型。
关键外卖
- 自动肿瘤从企业元数据中生成一个起步语义层。
- Agents通过9个只读的MCP工具查询，加上CLI和API.
- NVIDIA在自己的测试中报告了90%的更快的肿瘤创造。
- 尚未公布文本到SQL的精确分数。
- NIM上的Nemotron默认，但接受任何OpenAI相容的终点。
检查一下GitHub回波，Eval带和MCP道克。 并加入我们的通讯。 等等，你在电报里吗？现在你也可以加入我们的电报。
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NVIDIA has released Auto Ontology, an open library under the Apache 2.0 license. NVIDIA Auto Ontology is a semantic layer framework that builds a context layer from enterprise metadata. Agents use that layer to write SQL against certified business definitions. The repository states the problem plainly: a fast revenue answer is worthless if the number is wrong.
TL;DR
- Library: Default LLM is nvidia/nemotron-3-nano-30b-a3b, with Nemotron embedding and reranking models.
- Runs on: Docker Compose for a local stack, or Kubernetes via Helm. Inference uses hosted NVIDIA NIM endpoints or self-hosted NIMs.
- Performance: Strong engineering, thin public evidence.
- Best: Ontology creation time cut by 90% in NVIDIA testing (self-reported).
- Worst: 0 published text-to-SQL accuracy scores. The eval repo ships a harness, not results.
- Bottom line: An open, MCP-ready context layer that any agent harness can query. But you must run the benchmarks yourself to trust it.
What is NVIDIA Auto Ontology?
It is a context layer that sits between an agent and the data warehouse. It tells the agent where data lives and what fields mean. It also says whether to use a field, how to use it, and where it came from.
Here are the five jobs:
- Discover: generate ontologies from metadata and connect existing semantic models.
- Ground: give agents trusted meaning for metrics, entities and relationships.
- Validate: check generated queries against certified definitions and lineage.
- Expose: serve context through MCP, CLI and API.
- Learn: fold certified corrections back into the ontology.
NVIDIA’s developer page frames the output as a starting ontology. Business experts still test, refine and certify it.
How does Auto Ontology work?
The stack has 4 core services. A Next.js 16 frontend handles the web UI and authentication on port 3000. A FastAPI backend with LangChain runs chat, NL-to-SQL and catalog APIs on port 3001. An ingestion worker writes embeddings on port 3002. Postgres with pgvector stores the catalog, the ontology and the vectors.
Source databases can be Databricks, Postgres, Snowflake or DuckDB. Models are set through environment “triplets” of endpoint, key and model. Separate roles exist for reasoning, non-reasoning, embedding and reranking. Any OpenAI-compatible endpoint works.
What does the MCP server expose?
The MCP server docs describe 9 read-only tools. ask_question returns the answer, the SQL and the rows as separate fields. Results cap at 100 rows. check_readiness confirms a compiled semantic layer and a live database connection exist.
There is no tool for browsing raw schemas. NVIDIA chose that on purpose, so agents reach data through the semantic layer. The underlying OpenAPI spec publishes 87 operations. Sign-in runs through OAuth, and every call carries the signed-in user’s permissions.
How is it benchmarked?
NVIDIA ships a separate harness, auto-ontology-eval. It runs 2 arms in NeMo Gym: a schema-only LLM control and the ontology-grounded agent. Scoring is deterministic execution match, with no LLM judge.
Bundled datasets include BIRD Dev (1,534 questions, 11 databases) and Mini-Dev (500 questions). FDABench-Lite adds 169 questions, and BEAVER defaults to a 100-question sample.
The harness repo adds 2 limitations. Optional few-shot exemplars and value anchors go to the ontology arm only. A delta measured with them covers those inputs, not grounding alone. Its tested configuration also routes both arms through a Claude model on NVIDIA’s inference gateway.
How does it compare with other semantic layers?
Deployment and availability
Local setup is docker compose up -d --build after filling in .env. Kubernetes users get a Helm chart and a DEPLOYMENT.md guide. NVIDIA team also publishes the backend as an NGC container. An NVIDIA API key from build.nvidia.com covers the default models.
Key Takeaways
- Auto Ontology generates a starting semantic layer from enterprise metadata.
- Agents query it through 9 read-only MCP tools, plus CLI and API.
- NVIDIA reports 90% faster ontology creation in its own testing.
- No text-to-SQL accuracy scores are published yet.
- Defaults to Nemotron on NIM, but accepts any OpenAI-compatible endpoint.
Check out the GitHub repo, the eval harness and the MCP docs. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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