Implement data-source access control for SQL agent; add caching for user access profiles and enhance query processing with access validation

Refactor agents to use asynchronous clients, enhance SQL execution with connection pooling, and improve rate-limiting logic

Refactor SQL queries to improve aliasing for clarity and consistency

Refactor SQL queries to improve readability and consistency in naming conventions

Increase max_tokens limit for SQLAgent to enhance response capacity

Refactor SQL example inputs to use consistent aliasing for clarity and readability

Revert "Clarify SQL example inputs in system prompt to enhance user understanding and ensure consistency in query structure"

Clarify SQL example inputs in system prompt to enhance user understanding and ensure consistency in query structure

Revise SQL response prompts to clarify the order of results presentation, emphasizing business-friendly summaries followed by markdown tables.

Strip LLM preamble from SQL statements by identifying the start of the SQL query

Enhance SQL response formatting by requiring markdown tables for query results in prompts

Enhance SQL parsing to support JSON responses and extract corrected SQL

Enhance multi-agent pipeline with conversation memory and feedback logging

Refactor: Remove deprecated agent modules and chat endpoints

feat: Set TensorFlow environment variable for optimized performance

Refactor code structure for improved readability and maintainability