[Proposal] Enable AI-Native Metadata Governance

[hdygxsj]

Context

As data ecosystems grow increasingly complex—spanning multiple engines (Trino, Spark, Flink), table formats (Paimon, Iceberg, Hudi) — I believe metadata management must evolve beyond passive cataloging. Gravitino has a unique opportunity to become an AI-native metadata governance platform that proactively helps users design, discover, secure, and optimize their data assets.

I’d like to propose integrating LangChain4j (the Java-native implementation of LangChain) to unlock intelligent, LLM-powered capabilities directly within Gravitino’s metadata layer.

Capabilities I’d Like to See

Post-Creation AI Assessment of Table Design

After a table is created (e.g., via DDL), I propose triggering an asynchronous AI evaluation to assess:
Partitioning strategy
Indexing opportunities
Format and storage options—especially Paimon-specific configurations like bucket, changelog-producer, and merge-engine
The system could then provide actionable, natural-language recommendations to improve performance, cost, and correctness.

Semantic Auto-Tagging of Tables and Columns

I suggest using LLMs and embedding models to automatically infer and apply standardized tags based on:
Column/table names (user_id, ssn, risk_score)
Business context (via RAG over internal glossaries or compliance policies)
Examples: fee-amount, price-amount, cost-amount

RAG-Powered Detection of Similar Tables

To reduce redundancy, I’d like Gravitino to detect semantically similar existing tables across catalogs when a new table is being created.

By building a vector index of table embeddings (schema + description + usage patterns), the system could, on CREATE TABLE, retrieve similar tables and generate a comparison report via LLM:
“A similar table web_events already exists (92% similarity). Consider reusing or merging.”

Natural Language Table Understanding (NL2Insight)

I envision users asking questions like:
“Which tables contain monetary or amount-related fields?”
“Where is customer order information stored?”
“Show me tables with user behavior logs from mobile apps.”
“Do we have any table tracking refund events?”

Just some initial ideas—feel free to join the discussion!

[jerryshao]

Looks great, @hdygxsj . Do you have any design doc about this?

[hdygxsj]

Looks great, @hdygxsj . Do you have any design doc about this?

I'm currently working on the draft and the design document of this proposal , and I expect to complete them by the end of this month or early January.

[hdygxsj]

I've submitted a simple demo at #9495 that illustrates how to use LangChain4j to enable an LLM to evaluate table schemas right after table creation. In the PR, I’ve also listed many remaining tasks needed to fully implement this feature. I’ll continue refining this draft and its design—feel free to share any thoughts or suggestions at any time!

[shaofengshi]

Good suggestion. This seems be a built-in AI agent, which can do some thing on certain event. If making it configurable (including the prompt, the event that trigger it, the output), that would be flexible and applicable for the user.

[hdygxsj]

Good suggestion. This seems be a built-in AI agent, which can do some thing on certain event. If making it configurable (including the prompt, the event that trigger it, the output), that would be flexible and applicable for the user.

This is truly a more holistic and architecturally comprehensive perspective. I will take your suggestion and further refine my proposal and design—for example, by introducing the concept of an LLMTrigger to manage how we can further empower our metadata lifecycle through LLMs.