Testing Natural Lang SQL

app.py

@@ -1,43 +1,68 @@
-import os
-import gradio as gr
-from openai import OpenAI
-from huggingface_hub import InferenceClient
+from langchain_community.utilities import SQLDatabase
+from langchain_openai import ChatOpenAI
+from langchain_community.agent_toolkits import create_sql_agent
+import pandas as pd
+from sqlalchemy import create_engine
+from datetime import datetime, timedelta
 
+# Sample Jira data structure
+jira_data = {
+    'ticket_id': ['PROJ-1', 'PROJ-2', 'PROJ-3', 'PROJ-4'],
+    'summary': ['Bug in login', 'Feature request', 'Performance issue', 'Security bug'],
+    'status': ['Closed', 'Open', 'In Progress', 'Closed'],
+    'priority': ['P1', 'P3', 'P2', 'P1'],
+    'severity': ['Critical', 'Low', 'Medium', 'Critical'],
+    'created_date': ['2024-01-01', '2024-01-05', '2024-01-10', '2024-01-15'],
+    'closed_date': ['2024-01-03', None, None, '2024-01-16'],
+    'resolution_time_hours': [48, None, None, 24],
+    'assignee': ['john', 'jane', 'bob', 'alice']
+}
 
-# Initialize HF Router client using OpenAI SDK
-'''
-client = OpenAI(
-    base_url="https://router.huggingface.co/v1",
-    api_key=os.environ["HF_TOKEN"],   # ensure HF_TOKEN is set
-)
-'''
-
-client = InferenceClient(api_key=os.environ["HF_TOKEN"])
-
-# LLM function
-def ask_llm(prompt):
-    try:
-        completion = client.chat.completions.create(
-            model="meta-llama/Llama-3.1-8B-Instruct",
-            messages=[
-                {"role": "user", "content": prompt}
-            ],
-            max_tokens=200,
-            temperature=0.7
-        )
-        #return completion.choices[0].message["content"]
-        return completion.choices[0].message.content
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-# Build Gradio UI
-demo = gr.Interface(
-    fn=ask_llm,
-    inputs=gr.Textbox(lines=3, label="Ask the AI"),
-    outputs=gr.Textbox(label="Response"),
-    title="HF Inference Client LLM Demo",
-    description="Powered by HuggingFace InferenceClient SDK."
+df = pd.DataFrame(jira_data)
+df['created_date'] = pd.to_datetime(df['created_date'])
+df['closed_date'] = pd.to_datetime(df['closed_date'])
+
+# Save to SQLite
+engine = create_engine("sqlite:///jira.db")
+df.to_sql("tickets", engine, if_exists="replace", index=False)
+
+# Create agent with table description
+db = SQLDatabase(engine)
+
+# Add table descriptions for better context
+table_info = """
+The 'tickets' table contains Jira ticket data with columns:
+- ticket_id: Unique ticket identifier (e.g., PROJ-123)
+- summary: Brief description of the ticket
+- status: Current status (Open, In Progress, Closed)
+- priority: Priority level (P1=Highest, P2=High, P3=Medium, P4=Low)
+- severity: Severity level (Critical, High, Medium, Low)
+- created_date: When ticket was created
+- closed_date: When ticket was closed (NULL if still open)
+- resolution_time_hours: Time taken to resolve in hours
+- assignee: Person assigned to ticket
+"""
+
+llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
+agent = create_sql_agent(
+    llm, 
+    db=db, 
+    verbose=True,
+    agent_type="openai-tools"
 )
 
-demo.launch()
+# Query examples
+questions = [
+    "What is the average resolution time?",
+    "How many tickets are open vs closed?",
+    "Show distribution of tickets by severity",
+    "Which assignee has the most P1 tickets?",
+    "How many critical tickets were resolved in less than 48 hours?",
+]
+
+for q in questions:
+    print(f"\n{'='*60}")
+    print(f"Q: {q}")
+    print(f"{'='*60}")
+    result = agent.invoke(q)
+    print(f"A: {result['output']}\n")

app.py.bak

@@ -0,0 +1,43 @@
+import os
+import gradio as gr
+from openai import OpenAI
+from huggingface_hub import InferenceClient
+
+
+# Initialize HF Router client using OpenAI SDK
+'''
+client = OpenAI(
+    base_url="https://router.huggingface.co/v1",
+    api_key=os.environ["HF_TOKEN"],   # ensure HF_TOKEN is set
+)
+'''
+
+client = InferenceClient(api_key=os.environ["HF_TOKEN"])
+
+# LLM function
+def ask_llm(prompt):
+    try:
+        completion = client.chat.completions.create(
+            model="meta-llama/Llama-3.1-8B-Instruct",
+            messages=[
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=200,
+            temperature=0.7
+        )
+        #return completion.choices[0].message["content"]
+        return completion.choices[0].message.content
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# Build Gradio UI
+demo = gr.Interface(
+    fn=ask_llm,
+    inputs=gr.Textbox(lines=3, label="Ask the AI"),
+    outputs=gr.Textbox(label="Response"),
+    title="HF Inference Client LLM Demo",
+    description="Powered by HuggingFace InferenceClient SDK."
+)
+
+demo.launch()