[Proposal] Enable AI-Native Metadata Governance

## 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!

This proposal does not name files, tests, or a bounded entry point. Start by reviewing Gravitino's metadata layer and the feasibility of integrating LangChain4j, then define a focused capability and its acceptance criteria before implementation. Done would require an agreed scope, implementation plan, and tests for that capability.

Written by the indexing model from the issue text.