Update Agent response in NL

1. Update main.py to separate the interactive logic keeping it modular.
2. Create new file (interactive_mode.py) to handle conversational mode.
3. Update sql_agent.py to include NL response template so system return NL response to user with data needed instead of only generated SQL.
4. Update evaluate_hf.py -> evaluation_mode.py to make naming convention consistent.

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "postman.settings.dotenv-detection-notification-visibility": false
+}

app/main.py

@@ -2,8 +2,8 @@
 # Main entry point for the NL2SQL application
 import os
 from dotenv import load_dotenv
-from src.nl2sql.sql_agent import nl2sql_agent
-from src.scripts.evaluate_hf import run_evaluation
+from src.scripts.interactive_mode import run_interactiveMode
+from src.scripts.evaluation_mode import run_evaluation
 
 load_dotenv()
 # Load HuggingFace API token from environment variable
@@ -11,32 +11,6 @@ hf_token = os.getenv("HF_TOKEN")
 if not hf_token:
     raise ValueError("HuggingFace API token not found!")
 
-# User prompt question manually and see the agent's response
-def interactive_mode():
-    """Allows user to manually type questions and get agent's response."""
-    print("\n========= Interactive NL2SQL Mode =========")
-    print("Type 'exit' or 'q' to return to the main menu.\n")
-
-    while True:
-        question = input("\nEnter your question: ")
-        if question.lower() in ['exit', 'q']:
-            break
-        if not question.strip():
-            continue
-
-        print("\nProcessing your question...")
-        response = nl2sql_agent(question)
-
-        print("\n========= Agent Response =========")
-        print(f"Status: {response.get('status')}")
-        print(f"Generated SQL:\n{response.get('query')}")
-
-        if response.get('status') == 'success':
-            print(f"\nresults (First 5 rows):\n{response.get('results')[:5]}")
-        else:
-            print(f"\nError Details:\n{response.get('error')}")
-        print("==================================\n")
-
 def main():
     """Main application entry point and interactive menu"""
     while True:
@@ -51,7 +25,7 @@ def main():
         choice = input("Select an option (1-3): ")
 
         if choice == '1':
-            interactive_mode()
+            run_interactiveMode()
         elif choice == '2':
             run_evaluation()
         elif choice == '3':

src/nl2sql/sql_agent.py

@@ -40,6 +40,16 @@ Do not include any explanations, markdown formatting, or code blocks.
 
 Corrected SQL Query:"""
 
+# Generate text response
+NL_RESPONSE_TEMPLATE = """You are a helpful data assisstant.
+The user asked the following question: "{question}"
+The database returned the following results: {results}
+
+Provide a direct, natural language answer to the user's question using ONLY the provided data.
+Keep it brief. Do not explain the SQL query or mention the database schema.
+
+Answer:"""
+
 prompt_template = PromptTemplate(
     input_variables = ["schema", "question"],
     template = SQL_PROMPT_TEMPLATE
@@ -50,6 +60,11 @@ refinement_prompt = PromptTemplate(
     template = REFINEMENT_PROMPT_TEMPLATE
 )
 
+nl_response_template = PromptTemplate(
+    input_variables = ["question", "results"],
+    template = NL_RESPONSE_TEMPLATE
+)
+
 # Clean the output
 def clean_sql(raw_sql: str) -> str:
     """
@@ -83,6 +98,7 @@ def nl2sql_agent(user_question: str, max_retries: int = 3) -> dict:
     # LangChain Pipeline: Pipe prompt into LLM
     chain = prompt_template | llm
     refinement_chain = refinement_prompt | llm
+    nl_chain = nl_response_template | llm
 
     current_sql = ""
     error_message = ""
@@ -126,10 +142,18 @@ def nl2sql_agent(user_question: str, max_retries: int = 3) -> dict:
                 
                 if attempt > 1:
                     print(f"SQL query executed successfully after {attempt} attempts.")
+                
+                # Generate natural language response based on the results
+                print("Generating natural language response based on query results...")
+                nl_response = nl_chain.invoke({
+                    "question": user_question,
+                    "results": str(results)
+                })
 
                 return {
                     "query": generated_sql,
                     "results": results,
+                    "nl_response": nl_response,
                     "status": "success",
                     "attempts": attempt
                 }

src/scripts/{evaluate_hf.py → evaluation_mode.py}

@@ -1,6 +1,5 @@
-# src/scripts/evaluate_hf.py
+# Path: src/scripts/evaluation_mode.py
 # Evaluation script for Hugging Face SQL generation.
-
 import json
 from pathlib import Path
 import pandas as pd

src/scripts/interactive_mode.py

@@ -0,0 +1,28 @@
+# Path: src/scripts/interactive_mode.py
+# Interactive mode: Allows user to manually type questions and see the agent's response
+from src.nl2sql.sql_agent import nl2sql_agent
+
+def run_interactiveMode():
+    """ Allows user to manually type questions and get agent's response."""
+    print("\n========= Interactive NL2SQL Mode =========")
+    print("Type 'exit' or 'q' to return to the main menu.\n")
+
+    while True:
+        question = input("\nEnter your question: ")
+        if question.lower() in ['exit', 'q']:
+            break
+        if not question.strip():
+            continue
+
+        print("\nProcessing your question...")
+        response = nl2sql_agent(question)
+
+        print("\n========= Agent Response =========")
+        if response.get('status') == 'success':
+            print(f"Answer: {response.get('nl_response')}\n")
+            print(f"Generated SQL:\n{response.get('query')}\n")
+        else:
+            print(f"Status: {response.get('status')}")
+            print(f"Generated SQL:\n{response.get('query')}")
+            print(f"\nError Details:\n{response.get('error')}")
+        print("==================================\n")