code: from langchain_openai import ChatOpenAI
  from langchain.chains import LLMChain
  from langchain.prompts import MessagesPlaceholder, HumanMessagePromptTemplate, ChatPromptTemplate
  from langchain.memory import ConversationBufferMemory, FileChatMessageHistory
  from dotenv import load_dotenv
  import sqlite3
  import sqlparse
  import json
  import warnings
  from langchain_core._api.deprecation import LangChainDeprecationWarning
  
  # Ignore specific deprecation warnings from langchain_core
  warnings.filterwarnings("ignore", category=LangChainDeprecationWarning)
  
  
  load_dotenv()
  
  # Create and initialize the OpenAI client
  def create_openai_client():
      return ChatOpenAI(model_name="gpt-3.5-turbo")
  
  # Retrieve the database schema from an SQLite database
  def get_database_schema(db_path):
      try:
          conn = sqlite3.connect(db_path)
          cursor = conn.cursor()
          cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
          tables = cursor.fetchall()
  
          schema_info = []
          for table in tables:
              table_name = table[0]
              cursor.execute(f"PRAGMA table_info({table_name});")
              columns = cursor.fetchall()
              primary_keys = [col[1] for col in columns if col[5] == 1]
              schema_info.append(f"{table_name} (Primary Key: {', '.join(primary_keys)})")
  
          conn.close()
          return "filename: chinook.db, "+', '.join(schema_info)
      except Exception as e:
          return f"Error reading database schema: {e}"
  
  # Format the raw SQL query for better readability using sqlparse
  def format_sql_query(raw_sql):
      if raw_sql.startswith("```sql"):
          raw_sql = raw_sql[6:]
      if raw_sql.endswith("```"):
          raw_sql = raw_sql[:-3]
      raw_sql = raw_sql.strip()
      formatted_sql = sqlparse.format(raw_sql, reindent=True, keyword_case='upper')
      return formatted_sql
  
  # Function to execute a SQL query and return results
  def execute_sql_query(db_path, sql_query):
      try:
          conn = sqlite3.connect(db_path)
          cursor = conn.cursor()
          cursor.execute(sql_query)
          results = cursor.fetchall()
          conn.close()
          return results
      except sqlite3.Error as e:
          return f"SQL Error: {e}"
  
  
  # Main execution
  if __name__ == "__main__":
      client = create_openai_client()
      db_path = "chinook.db"
      database_schema = get_database_schema(db_path)
  
      memory = ConversationBufferMemory(
          chat_memory=FileChatMessageHistory("chat_history.json"),
          memory_key="messages",
          return_messages=True,
      )
  
      prompt = ChatPromptTemplate(
          input_variables=["content", "messages"],
          messages=[
              MessagesPlaceholder(variable_name="messages"),
              HumanMessagePromptTemplate.from_template(
                  "You are a data analyst. Generate SQL queries from natural language descriptions. Provide only the SQL query in response, without explanations or additional text"
                  "Question: {{question}} Database Schema: {{database_schema}} Response: {content}"
              ),
          ],
      )
  
      chain = LLMChain(
          llm=client,
          prompt=prompt,
          memory=memory,
      )
  
      while True:
          content = input("Usage: type 'quit' to exit chat\n>> ")
  
          if content == "quit":
              break
  
          result = chain({"content": content})
  
          # Format the SQL query
          formatted_query = format_sql_query(result['text'])
          print("\nGenerated SQL Query:\n", formatted_query)
  
          # Execute the SQL query and print results
          query_results = execute_sql_query(db_path, formatted_query)
          print("\nQuery Results:\n", query_results)
apis: langchain.memory.FileChatMessageHistory langchain.chains.LLMChain langchain_openai.ChatOpenAI langchain.prompts.MessagesPlaceholder langchain.prompts.HumanMessagePromptTemplate.from_template
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