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import datetime
from typing import List, Dict, Any
class Chatbot:
"""
A Chatbot class to manage chat conversations using an LLM service and a database to store chat data.
Methods:
- start_chat: Starts a new conversation, logs the start time.
- handle_user_message: Processes user input and stores user message & bot response in DB.
- end_chat: Ends the conversation and logs the end time.
- continue_chat: Retains only the last few messages if the conversation exceeds 1000 messages.
"""
def __init__(self, db: Any, llm_service: Any) -> None:
"""
Initialize the Chatbot with a database and an LLM service.
Parameters:
- db: The database instance used for storing chat data.
- llm_service: The language model service for generating responses.
"""
self.db = db
self.llm_service = llm_service
self.conversation_history: List[Dict[str, str]] = []
self.chat_id_pk: int = None
def start_chat(self) -> None:
"""
Start a new chat session and insert chat history to the database.
"""
start_time = datetime.datetime.now()
is_stream = 1 # Start new conversation
self.db.insert_chat_history(start_time, is_stream)
self.chat_id_pk = self.db.get_latest_chat_id()
def handle_user_message(self, user_input: str) -> str:
"""
Handle user input and generate a bot response.
If the user sends '/stop', the conversation is terminated.
Parameters:
- user_input: The input provided by the user.
Returns:
- bot_response: The response generated by the bot.
Raises:
- ValueError: If user input is not a string or if no chat_id is available.
Doctest:
>>> class MockDatabase:
... def __init__(self):
... self.data = []
... def insert_chat_data(self, *args, **kwargs):
... pass
... def insert_chat_history(self, *args, **kwargs):
... pass
... def get_latest_chat_id(self):
... return 1
...
>>> class MockLLM:
... def generate_response(self, conversation_history):
... if conversation_history[-1]["content"] == "/stop":
... return "conversation-terminated"
... return "Mock response"
>>> db_mock = MockDatabase()
>>> llm_mock = MockLLM()
>>> bot = Chatbot(db_mock, llm_mock)
>>> bot.start_chat()
>>> bot.handle_user_message("/stop")
'conversation-terminated'
>>> bot.handle_user_message("Hello!")
'Mock response'
"""
if not isinstance(user_input, str):
raise ValueError("User input must be a string.")
if self.chat_id_pk is None:
raise ValueError("Chat has not been started. Call start_chat() first.")
self.conversation_history.append({"role": "user", "content": user_input})
if user_input == "/stop":
self.end_chat()
return "conversation-terminated"
else:
bot_response = self.llm_service.generate_response(self.conversation_history)
print(f"Bot : ",bot_response)
self.conversation_history.append(
{"role": "assistant", "content": bot_response}
)
self._store_message_in_db(user_input, bot_response)
return bot_response
def _store_message_in_db(self, user_input: str, bot_response: str) -> None:
"""
Store user input and bot response in the database.
Parameters:
- user_input: The message from the user.
- bot_response: The response generated by the bot.
Raises:
- ValueError: If insertion into the database fails.
"""
try:
self.db.insert_chat_data(self.chat_id_pk, user_input, bot_response)
except Exception as e:
raise ValueError(f"Failed to insert chat data: {e}")
def end_chat(self) -> None:
"""
End the chat session and update the chat history in the database.
"""
current_time = datetime.datetime.now()
is_stream = 2 # End of conversation
try:
user_input = "/stop"
bot_response = "conversation-terminated"
print(f"Bot : ",bot_response)
self.db.insert_chat_data(self.chat_id_pk, user_input, bot_response)
self.db.insert_chat_history(current_time, is_stream)
except Exception as e:
raise ValueError(f"Failed to update chat history: {e}")
def continue_chat(self) -> None:
"""
Retain only the last few entries if the conversation exceeds 1000 messages.
"""
if len(self.conversation_history) > 1000:
self.conversation_history = self.conversation_history[-3:]

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import os
from dotenv import load_dotenv
import mysql.connector
from mysql.connector import MySQLConnection
load_dotenv()
class Database:
"""
A class to manage the connection to the MySQL database using configuration from environment variables.
Attributes:
-----------
config : dict
The database connection parameters like user, password, host, and database name.
"""
def __init__(self) -> None:
self.config = {
"user": os.environ.get("DB_USER"),
"password": os.environ.get("DB_PASSWORD"),
"host": os.environ.get("DB_HOST"),
"database": os.environ.get("DB_NAME"),
}
def connect(self) -> MySQLConnection:
"""
Establish a connection to the MySQL database.
Returns:
--------
MySQLConnection
A connection object for interacting with the MySQL database.
Raises:
-------
mysql.connector.Error
If the connection to the database fails.
"""
return mysql.connector.connect(**self.config)
class ChatDatabase:
"""
A class to manage chat-related database operations, such as creating tables,
inserting chat history, and retrieving chat data.
Attributes:
-----------
db : Database
An instance of the `Database` class for establishing connections to the MySQL database.
"""
def __init__(self, db: Database) -> None:
self.db = db
def create_tables(self) -> None:
"""
Create the necessary tables for chat history and chat data in the database.
If the tables already exist, they will not be created again.
Raises:
-------
mysql.connector.Error
If there is any error executing the SQL statements.
"""
conn = self.db.connect()
cursor = conn.cursor()
cursor.execute(
"""
CREATE TABLE IF NOT EXISTS ChatDB.Chat_history (
chat_id INT AUTO_INCREMENT PRIMARY KEY,
start_time DATETIME,
is_stream INT
)
"""
)
cursor.execute(
"""
CREATE TABLE IF NOT EXISTS ChatDB.Chat_data (
id INT AUTO_INCREMENT PRIMARY KEY,
chat_id INT,
user TEXT,
assistant TEXT,
FOREIGN KEY (chat_id) REFERENCES ChatDB.Chat_history(chat_id)
)
"""
)
cursor.execute("DROP TRIGGER IF EXISTS update_is_stream")
cursor.execute(
"""
CREATE TRIGGER update_is_stream
AFTER UPDATE ON ChatDB.Chat_history
FOR EACH ROW
BEGIN
UPDATE ChatDB.Chat_data
SET is_stream = NEW.is_stream
WHERE chat_id = NEW.chat_id;
END;
"""
)
conn.commit()
cursor.close()
conn.close()
def insert_chat_history(self, start_time: str, is_stream: int) -> None:
"""
Insert a new chat history record into the database.
Parameters:
-----------
start_time : str
The starting time of the chat session.
is_stream : int
An integer indicating whether the chat is in progress (1) or ended (2).
Raises:
-------
mysql.connector.Error
If there is any error executing the SQL statements.
"""
conn = self.db.connect()
cursor = conn.cursor()
cursor.execute(
"""
INSERT INTO ChatDB.Chat_history (start_time, is_stream)
VALUES (%s, %s)
""",
(start_time, is_stream),
)
conn.commit()
cursor.close()
conn.close()
def get_latest_chat_id(self) -> int:
"""
Retrieve the chat ID of the most recent chat session from the database.
Returns:
--------
int
The ID of the latest chat session.
Raises:
-------
mysql.connector.Error
If there is any error executing the SQL statements.
"""
conn = self.db.connect()
cursor = conn.cursor()
cursor.execute(
"""
SELECT chat_id FROM ChatDB.Chat_history WHERE
chat_id=(SELECT MAX(chat_id) FROM ChatDB.Chat_history)
"""
)
chat_id_pk = cursor.fetchone()[0]
cursor.close()
conn.close()
return chat_id_pk
def insert_chat_data(
self, chat_id: int, user_message: str, assistant_message: str
) -> None:
"""
Insert a new chat data record into the database.
Parameters:
-----------
chat_id : int
The ID of the chat session to which this data belongs.
user_message : str
The message provided by the user in the chat session.
assistant_message : str
The response from the assistant in the chat session.
Raises:
-------
mysql.connector.Error
If there is any error executing the SQL statements.
"""
conn = self.db.connect()
cursor = conn.cursor()
cursor.execute(
"""
INSERT INTO ChatDB.Chat_data (chat_id, user, assistant)
VALUES (%s, %s, %s)
""",
(chat_id, user_message, assistant_message),
)
conn.commit()
cursor.close()
conn.close()

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import os
from together import Together
from groq import Groq
from dotenv import load_dotenv
from typing import List, Dict
load_dotenv()
class LLMService:
"""
A class to interact with different LLM (Large Language Model) API services, such as Together and Groq.
Attributes:
-----------
api_service : str
The name of the API service to use ("Together" or "Groq").
"""
def __init__(self, api_service: str) -> None:
"""
Initialize the LLMService with a specific API service.
Parameters:
-----------
api_service : str
The name of the LLM API service, either "Together" or "Groq".
"""
self.api_service = api_service
def generate_response(self, conversation_history: List[Dict[str, str]]) -> str:
"""
Generate a response from the specified LLM API based on the conversation history.
Parameters:
-----------
conversation_history : List[Dict[str, str]]
The list of conversation messages, where each message is a dictionary with 'role' and 'content' keys.
Returns:
--------
str
The generated response content from the assistant.
Raises:
-------
ValueError
If the specified API service is neither "Together" nor "Groq".
"""
if self.api_service == "Together":
client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
response = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct-Turbo",
messages=conversation_history,
max_tokens=512,
temperature=0.3,
top_p=0.7,
top_k=50,
repetition_penalty=1,
stop=["<|eot_id|>", "<|eom_id|>"],
stream=False,
)
elif self.api_service == "Groq":
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
response = client.chat.completions.create(
model="llama3-8b-8192",
messages=conversation_history,
max_tokens=1024,
temperature=0.3,
top_p=0.7,
stop=["<|eot_id|>", "<|eom_id|>"],
stream=False,
)
else:
raise ValueError(f"Unsupported API service: {self.api_service}")
# Extracting the content of the generated response
return response.choices[0].message.content

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from db import Database, ChatDatabase
from llm_service import LLMService
from chatbot import Chatbot
from typing import NoReturn
def main() -> NoReturn:
"""
Main function to initialize and start the chatbot application.
This function initializes the database and LLM service, creates necessary tables, and starts
the chatbot for user interaction.
"""
# Initialize and configure the database
db = Database()
chat_db = ChatDatabase(db)
chat_db.create_tables()
# Set the API service to either "Together" or "Groq"
api_service = (
"Groq" # Can be set dynamically based on user preference or environment
)
llm_service = LLMService(api_service)
# Initialize the Chatbot with the database and LLM service
chatbot = Chatbot(chat_db, llm_service)
print("Welcome to the chatbot! Type '/stop' to end the conversation.")
chatbot.start_chat()
# Chat loop to handle user input
while True:
user_input = input("\nYou: ")
if user_input.strip().lower() == "/stop":
chatbot.end_chat() # End the conversation if user types "/stop"
break
chatbot.handle_user_message(
user_input
) # Process user input and generate response
chatbot.continue_chat() # Handle long conversations (trim history if necessary)
if __name__ == "__main__":
main()

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aiohappyeyeballs==2.4.2
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
asgiref==3.8.1
attrs==24.2.0
black==24.10.0
certifi==2024.8.30
cfgv==3.4.0
charset-normalizer==3.3.2
click==8.1.7
distlib==0.3.9
distro==1.9.0
Django==5.1.1
djangorestframework==3.15.2
eval_type_backport==0.2.0
filelock==3.16.1
frozenlist==1.4.1
groq==0.11.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
identify==2.6.1
idna==3.10
markdown-it-py==3.0.0
mdurl==0.1.2
multidict==6.1.0
mypy-extensions==1.0.0
mysql-connector-python==9.0.0
nodeenv==1.9.1
numpy==2.1.1
packaging==24.1
pathspec==0.12.1
pillow==10.4.0
platformdirs==4.3.6
pre_commit==4.0.1
pyarrow==17.0.0
pydantic==2.9.2
pydantic_core==2.23.4
Pygments==2.18.0
python-dotenv==1.0.1
PyYAML==6.0.2
requests==2.32.3
rich==13.8.1
ruff==0.7.0
shellingham==1.5.4
sniffio==1.3.1
sqlparse==0.5.1
tabulate==0.9.0
together==1.3.0
tqdm==4.66.5
typer==0.12.5
typing_extensions==4.12.2
urllib3==2.2.3
virtualenv==20.27.0
yarl==1.13.1