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Pritam Das
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neural_network/chatbot/chat_db.py
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236
neural_network/chatbot/chat_db.py
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import os
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import mysql.connector
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import datetime
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from dotenv import load_dotenv
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from together import Together
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from groq import Groq
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load_dotenv()
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# Database configuration
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db_config = {
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"user": os.environ.get("DB_USER"),
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"password": os.environ.get("DB_PASSWORD"),
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"host": os.environ.get("DB_HOST"),
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"database": os.environ.get("DB_NAME"),
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}
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api_service = os.environ.get("API_SERVICE")
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def create_tables() -> None:
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"""
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Create the ChatDB.Chat_history and ChatDB.Chat_data tables if they do not exist.
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Also, create a trigger to update is_stream in Chat_data when Chat_history.is_stream is updated.
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"""
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try:
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conn = mysql.connector.connect(**db_config)
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cursor = conn.cursor()
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cursor.execute(
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"""
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CREATE TABLE IF NOT EXISTS ChatDB.Chat_history (
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chat_id INT AUTO_INCREMENT PRIMARY KEY,
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start_time DATETIME,
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is_stream INT
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)
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"""
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)
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cursor.execute(
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"""
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CREATE TABLE IF NOT EXISTS ChatDB.Chat_data (
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id INT AUTO_INCREMENT PRIMARY KEY,
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chat_id INT,
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user TEXT,
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assistant TEXT,
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FOREIGN KEY (chat_id) REFERENCES ChatDB.Chat_history(chat_id)
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)
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"""
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)
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cursor.execute("DROP TRIGGER IF EXISTS update_is_stream;")
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cursor.execute(
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"""
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CREATE TRIGGER update_is_stream
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AFTER UPDATE ON ChatDB.Chat_history
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FOR EACH ROW
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BEGIN
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UPDATE ChatDB.Chat_data
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SET is_stream = NEW.is_stream
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WHERE chat_id = NEW.chat_id;
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END;
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"""
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)
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conn.commit()
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except mysql.connector.Error as err:
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print(f"Error: {err}")
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finally:
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cursor.close()
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conn.close()
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print("Tables and trigger created successfully")
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def insert_chat_history(start_time: datetime.datetime, is_stream: int) -> None:
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"""
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Insert a new row into the ChatDB.Chat_history table.
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:param start_time: Timestamp of when the chat started
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:param is_stream: Indicator of whether the conversation is ongoing, starting, or ending
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"""
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try:
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conn = mysql.connector.connect(**db_config)
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cursor = conn.cursor()
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cursor.execute(
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"""
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INSERT INTO ChatDB.Chat_history (start_time, is_stream)
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VALUES (%s, %s)
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""",
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(start_time, is_stream),
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)
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conn.commit()
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except mysql.connector.Error as err:
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print(f"Error: {err}")
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finally:
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cursor.close()
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conn.close()
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def get_latest_chat_id() -> int:
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"""
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Retrieve the latest chat_id from the ChatDB.Chat_history table.
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:return: The latest chat_id or None if no chat_id exists.
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"""
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try:
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conn = mysql.connector.connect(**db_config)
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cursor = conn.cursor()
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cursor.execute(
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"""
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SELECT chat_id FROM ChatDB.Chat_history
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ORDER BY chat_id DESC LIMIT 1
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"""
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)
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chat_id = cursor.fetchone()[0]
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return chat_id if chat_id else None
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except mysql.connector.Error as err:
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print(f"Error: {err}")
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return None
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finally:
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cursor.close()
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conn.close()
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def insert_chat_data(chat_id: int, user_message: str, assistant_message: str) -> None:
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"""
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Insert a new row into the ChatDB.Chat_data table.
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:param chat_id: The ID of the chat session
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:param user_message: The user's message
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:param assistant_message: The assistant's message
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"""
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try:
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conn = mysql.connector.connect(**db_config)
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cursor = conn.cursor()
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cursor.execute(
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"""
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INSERT INTO ChatDB.Chat_data (chat_id, user, assistant)
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VALUES (%s, %s, %s)
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""",
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(chat_id, user_message, assistant_message),
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)
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conn.commit()
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except mysql.connector.Error as err:
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print(f"Error: {err}")
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finally:
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cursor.close()
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conn.close()
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def generate_llm_response(
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conversation_history: list[dict], api_service: str = "Groq"
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) -> str:
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"""
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Generate a response from the LLM based on the conversation history.
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:param conversation_history: List of dictionaries representing the conversation so far
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:param api_service: Choose between "Together" or "Groq" as the API service
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:return: Assistant's response as a string
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"""
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bot_response = ""
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if api_service == "Together":
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client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
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response = client.chat.completions.create(
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model="meta-llama/Llama-3.2-3B-Instruct-Turbo",
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messages=conversation_history,
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max_tokens=512,
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temperature=0.3,
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top_p=0.7,
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top_k=50,
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repetition_penalty=1,
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stop=["<|eot_id|>", "<|eom_id|>"],
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stream=False,
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)
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bot_response = response.choices[0].message.content
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else:
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client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
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response = client.chat.completions.create(
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model="llama3-8b-8192",
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messages=conversation_history,
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max_tokens=1024,
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temperature=0.3,
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top_p=0.7,
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stop=["<|eot_id|>", "<|eom_id|>"],
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stream=False,
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)
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bot_response = response.choices[0].message.content
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return bot_response
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def chat_session() -> None:
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"""
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Start a chatbot session, allowing the user to interact with the LLM.
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Saves conversation history in the database and ends the session on "/stop" command.
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"""
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print("Welcome to the chatbot! Type '/stop' to end the conversation.")
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conversation_history = []
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start_time = datetime.datetime.now()
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chat_id_pk = None
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api_service = "Groq" # or "Together"
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while True:
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user_input = input("\nYou: ").strip()
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conversation_history.append({"role": "user", "content": user_input})
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if chat_id_pk is None:
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if user_input.lower() == "/stop":
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break
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bot_response = generate_llm_response(conversation_history, api_service)
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conversation_history.append({"role": "assistant", "content": bot_response})
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is_stream = 1 # New conversation
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insert_chat_history(start_time, is_stream)
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chat_id_pk = get_latest_chat_id()
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insert_chat_data(chat_id_pk, user_input, bot_response)
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else:
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if user_input.lower() == "/stop":
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is_stream = 2 # End of conversation
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current_time = datetime.datetime.now()
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insert_chat_history(current_time, is_stream)
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break
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bot_response = generate_llm_response(conversation_history, api_service)
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conversation_history.append({"role": "assistant", "content": bot_response})
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is_stream = 0 # Continuation of conversation
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current_time = datetime.datetime.now()
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insert_chat_history(current_time, is_stream)
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insert_chat_data(chat_id_pk, user_input, bot_response)
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if len(conversation_history) > 1000:
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conversation_history = conversation_history[-3:]
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# Example of starting a chat session
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create_tables()
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chat_session()
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