Creating a semantic model registry

github

sj gong • Peerlist

Service Introductionrawctx is an answer evidence layer that gives evidence of what official criteria, sources, and model execution contexts the AI responses to customers are based on. Enterprises can keep and verify answers from customer-facing chatbots, AI analysts, and copilots as question hash, answer hash, semantic refs, and prof bundle rather than just logarithms. rawctx does not guarantee the truthfulness of the answers, as it is not the layer to judge the model's outcome. Instead, it shows what criteria the answers were bound to, have not changed since, and can be verified by external anchors and witnesses. This makes it easier for security, law, customers, and auditors to review AI answers and obtain the evidence needed to approve them.---The question we're trying to solveWhen companies introduce customer-facing AI, answers are generated quickly, but it is difficult to explain what criteria and grounds the answers were made later. If the same KPI or policy is interpreted differently for each document, database, or prompt, and there is no standard version, source, or model execution context left at the time of the answer, reproducible audits are impossible during security review, legal response, and customer issue processing. It is difficult to objectively prove whether the answer has not changed since then or on what basis it is actually tied to with only internal logs. The problem rawctx is trying to solve is not to judge on behalf of AI, but rather a structural void that does not leave AI answers to customers as evidence that companies can verify, explain, and submit later.---A solution to a problemRawctx solves the problem by leaving the evidence and execution context that disappear after AI answers are generated as verifiable evidence. First, it versions the company's KPI definitions, policies, and document sources in a semantic package and records what criteria the AI referred to when answering them in the answer audit log. Then, the original text of the question and answer is sealed with a hash, and the execution context, such as the model and settings, and input/output hash, is grouped together to create a prof bundle. This evidence is connected to external verification paths such as Merkle, STH, OpenTimestamps, and Rekor to check for changes and confidence. As a result, security, legal, and customer representatives can review and approve AI answers as submissionable reports without re-explaining them or manually tracking them.

rawctx Answer Proof

Grammar might be weird because I'm not familiar with English.rawctx is a registry of semantic models, and it's a service that goes against this in a time when AI products are full. It's not using AI, it's a service that helps AI. think npm, but for semantic modelsSeveral companies recently agreed on a common format called Open Semantic Interchange (OSI) and released v1.0. rawctx is built to be the independent registry that no single vendor can own.The reason I created this is that the definition of semantic models is still ongoing, and I've seen the potential in that it's a format that can be shared.Currently, I'm working as a data engineer, modeling semantic layers at the company I'm working for. And I found out in this process that I need to share models within my team.However, it was difficult to measure the performance of each service on the same semantic model basis because the team was using various programs on a trial basis.So I thought, let's make a service where we can share this.That's why I made it. I have a few semantic models shared on a trial basis now, but I hope it will be a service that many people can use comfortably over time.In addition, since the semantic model is an important asset for a company, we have also prepared a space for it to be shared privately.install : pip install rawctxcommands : rawctx search "shopify revenue"rawctx info @dbt-hub/jaffle-shoprawctx snapshot-download @pasar6987/sf-revenue-asset &lt;&lt; downloadableGitHub administrators of indexed models can transfer ownership after login through rawctx claim.rawctx claim @github-hub/osi