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app.py

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+import os
+import gradio as gr
+
+from langchain.llms import AzureOpenAI
+from langchain.chat_models import AzureChatOpenAI
+from langchain.chains import SQLDatabaseChain
+from langchain.agents import Tool, AgentType, initialize_agent
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts.prompt import PromptTemplate
+
+from load_dotenv import load_dotenv
+from sqlalchemy import MetaData, create_engine, inspect, select, text
+
+# The file we created above
+from loader import load_database
+# Load the .env file to access the keys
+load_dotenv()
+
+# Set up the completion and chat llm (optional, experiment with this!)
+llm = AzureOpenAI(deployment_name="your_deployment_name", 
+                  model_name="text-davinci-003")
+chat_llm = AzureChatOpenAI(deployment_name="gpt-35-turbo",temperature=0.1) 
+
+# Set up the chat llm
+os.environ["OPENAI_API_VERSION"]=os.getenv('OPENAI_CHAT_API_VERSION')
+
+# Create engine and Call the function to ingest the data
+engine = create_engine('sqlite:///db', echo=True)
+db = load_database(engine)
+
+# OR 
+
+# if the database exists somewhere you could do something like;
+engine = create_engine("your custom URL, example - postgresql+psycopg2://scott:tiger@localhost:5432/mydatabase")
+db = load_database(engine)
+
+# Create an inspector object to inspect the database
+inspector = inspect(engine)
+
+# Get the list of table names
+table_names = inspector.get_table_names()
+
+# Create SQLDatabaseChain
+sql_chain = SQLDatabaseChain.from_llm(llm, db, 
+                  verbose=True, use_query_checker=True)
+
+
+# Create SQLDatabaseChain
+one_sql_chain = SQLDatabaseChain.from_llm(llm, car_db, 
+                  verbose=True, use_query_checker=True)
+
+two_sql_chain = SQLDatabaseChain.from_llm(llm, bike_db, 
+                  verbose=True, use_query_checker=True)
+
+
+
+memory = ConversationBufferMemory(memory_key="chat_history", 
+         return_messages=True)
+
+tools = [one_sql_tool, two_sql_tool]
+
+conversational_agent = initialize_agent(
+    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    tools=tools,
+    llm=llm,
+    verbose=True,
+    max_iterations=3,
+    memory=memory,
+    dialect='ansi',
+    early_stopping_method="generate",
+    handle_parsing_errors=True,
+)
+
+# Define a simple query function that runs the query agent and returns the response
+def query_fnr(input_text):
+    response = conversational_agent.run(input=input_text)
+    return response
+
+# Build the UI
+iface = gr.Interface(
+    fn=query_fn,
+    inputs=gr.inputs.Textbox(label="Enter your query"),
+    outputs=gr.outputs.Textbox(label="Query Result"),
+    title="Domain-specific chatbot"
+)
+
+# Launch the UI but do not share it publicly
+iface.launch(share=False, server_port=8080)

loader.py

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+from langchain import SQLDatabase
+import pandas as pd
+from sqlalchemy import create_engine, Column, Integer, String, Date
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+from datetime import datetime
+from sqlalchemy import create_engine, Column, String, Integer, Date
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+
+def create_demand_table(engine, table_name, excel_file):
+    # Read the Excel file
+    dataframes = pd.read_excel(excel_file, sheet_name=None)
+
+    # Create a base class for the table models
+    Base = declarative_base()
+
+    # Define the table model
+    class DemandPlanned(Base):
+        __tablename__ = table_name
+
+        KEY = Column(String, primary_key=True)
+        DU = Column(String)
+        ORIGIN = Column(String)
+        DESTINATION = Column(String)
+        DEMAND_PLANNED_QTY = Column(Integer)
+        DEMAND_PLANNED_DATE = Column(Date)
+
+    # Drop the existing table in the SQLite database, if it exists
+    Base.metadata.drop_all(engine)
+
+    # Create the table in the SQLite database
+    Base.metadata.create_all(engine)
+
+    # Create a session to interact with the database
+    Session = sessionmaker(bind=engine)
+
+    with Session() as session:
+        # Insert data into the table (db)
+        demand = dataframes.get('Demand-Planned')
+        if demand is not None:
+            demand['DEMAND_PLANNED_DATE'] = pd.to_datetime(demand['DEMAND_PLANNED_DATE']).dt.strftime('%Y-%m-%d')
+            demand.to_sql(table_name, con=engine, if_exists='append', index=False)
+
+    db = SQLDatabase(engine)
+
+    # Commit the changes to the production database
+    session.commit()
+
+    # Close the session
+    session.close()
+
+    return db