[Feat] Schema-aware enhancement for SQL Agent generation

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Hi @LinuxSuRen,

I'm currently exploring the `AI-Powered API Testing Agent` projects in OSPP, and I find the idea of using an AI agent to convert natural language into SQL really awesome!!! I believe it could greatly help non-technical users interact with data more naturally. (As a student, I often feel quite overwhelmed when working with complex or large datasets, and it can be really challenging...)

While reading through the project goals and thinking from a user perspective, I wondered:

Would it make sense to enrich the agent's SQL generation with some form of schema-awareness?

For example, if a user types:
"Show me all disabled accounts"

But the database schema has a field like `status = 'deactivated'` or `is_active = false`, a naive prompt-to-SQL conversion might not catch that. In this case, a retrieval step — even something lightweight like schema inspection or doc-based enrichment — might help improve both accuracy and robustness.

I'm still trying to understand the current architecture better, so this is more of a question than a suggestion. But I'd love to know:

Has this direction been considered before?Would you be open to exploring something like this in the future?

If this is a valuable direction, I'd be very interested in trying to contribute something along these lines after understanding the system more clearly.

Thanks again for your awesome work on this project! I’m really enjoying digging into it.

L’issue riguarda una domanda sulla futura generazione di SQL consapevole dello schema nell’AI-Powered API Testing Agent, ma non indica file, test o punti di ingresso. Inizia mappando l’architettura dell’agente e il flusso attuale di natural-language-to-SQL. Per considerarla completata servirebbero un design concordato e un miglioramento dimostrato nella generazione di SQL, ma questa issue non li definisce ancora.

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