v0.1.3

📦 SQL Agent Tool v0.1.3

This release introduces an interactive chat interface and enhanced LLM flexibility, empowering users to select their preferred language model for natural language to SQL conversion.

🔧 Updates

• Chat Interface: Added an interactive chat mode for real-time query input and execution.
• LLM Selection: Users can now choose from multiple LLM providers (Groq, Gemini, OpenAI, DeepSeek) to generate SQL, tailored to their needs.

📊 Sample Code

# Launch chat interface and select LLM

   #this are contents of test1.py for testing of tool
   from sql_agent_tool.model import SQLAgentTool, DatabaseConfig, LLMConfig

   config = DatabaseConfig(
      drivername="postgresql",
      username="postgres",
      password="password",
      host="localhost",
      port=5433,
      database="P2"
   )
   llm_config = LLMConfig(provider="gemini", api_key=LLM_API_KEY, model="models/gemini-1.5-flash", max_tokens=500)

   sql_tool = SQLAgentTool(config, llm_config)
   try:
      print("\nQuery 1:")
      q1_start = time.time()
      result = agent_tool.process_natural_language_query("what are top courses purchased by maximum students?")
      print(f"Query 1 total time: {time.time() - q1_start:.2f} seconds")
      if result.success:
         print(f"Query executed successfully, found {result.row_count} results:")
         for row in result.data:
               print(row)

      print("\nQuery 2:")
      q2_start = time.time()
      result2 = agent_tool.process_natural_language_query("Are there any student named harsh?")
      print(f"Query 2 total time: {time.time() - q2_start:.2f} seconds")
      if result2.success:
         print(f"Query executed successfully, found {result2.row_count} results:")
         for row in result2.data:
               print(row)
   except Exception as e:
      print(f"Error processing queries: {e}")
   finally:
      agent_tool.close()
      print(f"Total time: {time.time() - start_time:.2f} seconds")

📊 Sample Output

Selected LLM: Gemini
Enter your query: Show me all courses with the most purchases
Generated SQL:
SELECT c.id, c.title, COUNT(p.id) as purchase_count
FROM courses c
JOIN payments p ON c.id = p.course_id
GROUP BY c.id, c.title
ORDER BY purchase_count DESC
LIMIT 500;
Parameters: {}
Query executed successfully, found 14 results:
{'id': ..., 'title': 'Social Media Marketing (SMM)', 'purchase_count': 6}
...

Developed By - @Dadiya-Harsh, @Dadiya-Harsh-wappnet

🔗 View on PyPI

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Notes

• Version: Bumped to v0.1.3 since this is a significant feature update (chat interface + LLM choice) beyond the initial v0.1.2.
• Sample Code: Assumed test1.py now supports --chat and --llm flags for the chat interface and LLM selection. If your implementation differs (e.g., no CLI flags), let me know, and I’ll adjust it!
• Sample Output: Adapted from your latest Groq-based run, simplified for brevity.

Full Changelog: v0.1.2...v0.1.3