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neural_network/chatbot/chatbot.py
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134
neural_network/chatbot/chatbot.py
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import datetime
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from typing import List, Dict, Any
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class Chatbot:
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"""
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A Chatbot class to manage chat conversations using an LLM service and a database to store chat data.
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Methods:
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- start_chat: Starts a new conversation, logs the start time.
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- handle_user_message: Processes user input and stores user message & bot response in DB.
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- end_chat: Ends the conversation and logs the end time.
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- continue_chat: Retains only the last few messages if the conversation exceeds 1000 messages.
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"""
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def __init__(self, db: Any, llm_service: Any) -> None:
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"""
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Initialize the Chatbot with a database and an LLM service.
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Parameters:
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- db: The database instance used for storing chat data.
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- llm_service: The language model service for generating responses.
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"""
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self.db = db
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self.llm_service = llm_service
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self.conversation_history: List[Dict[str, str]] = []
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self.chat_id_pk: int = None
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def start_chat(self) -> None:
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"""
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Start a new chat session and insert chat history to the database.
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"""
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start_time = datetime.datetime.now()
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is_stream = 1 # Start new conversation
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self.db.insert_chat_history(start_time, is_stream)
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self.chat_id_pk = self.db.get_latest_chat_id()
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def handle_user_message(self, user_input: str) -> str:
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"""
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Handle user input and generate a bot response.
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If the user sends '/stop', the conversation is terminated.
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Parameters:
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- user_input: The input provided by the user.
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Returns:
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- bot_response: The response generated by the bot.
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Raises:
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- ValueError: If user input is not a string or if no chat_id is available.
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Doctest:
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>>> class MockDatabase:
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... def __init__(self):
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... self.data = []
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... def insert_chat_data(self, *args, **kwargs):
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... pass
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... def insert_chat_history(self, *args, **kwargs):
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... pass
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... def get_latest_chat_id(self):
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... return 1
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...
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>>> class MockLLM:
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... def generate_response(self, conversation_history):
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... if conversation_history[-1]["content"] == "/stop":
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... return "conversation-terminated"
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... return "Mock response"
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>>> db_mock = MockDatabase()
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>>> llm_mock = MockLLM()
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>>> bot = Chatbot(db_mock, llm_mock)
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>>> bot.start_chat()
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>>> bot.handle_user_message("/stop")
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'conversation-terminated'
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>>> bot.handle_user_message("Hello!")
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'Mock response'
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"""
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if not isinstance(user_input, str):
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raise ValueError("User input must be a string.")
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if self.chat_id_pk is None:
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raise ValueError("Chat has not been started. Call start_chat() first.")
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self.conversation_history.append({"role": "user", "content": user_input})
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if user_input == "/stop":
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self.end_chat()
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return "conversation-terminated"
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else:
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bot_response = self.llm_service.generate_response(self.conversation_history)
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print(f"Bot : ",bot_response)
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self.conversation_history.append(
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{"role": "assistant", "content": bot_response}
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)
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self._store_message_in_db(user_input, bot_response)
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return bot_response
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def _store_message_in_db(self, user_input: str, bot_response: str) -> None:
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"""
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Store user input and bot response in the database.
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Parameters:
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- user_input: The message from the user.
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- bot_response: The response generated by the bot.
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Raises:
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- ValueError: If insertion into the database fails.
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"""
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try:
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self.db.insert_chat_data(self.chat_id_pk, user_input, bot_response)
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except Exception as e:
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raise ValueError(f"Failed to insert chat data: {e}")
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def end_chat(self) -> None:
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"""
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End the chat session and update the chat history in the database.
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"""
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current_time = datetime.datetime.now()
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is_stream = 2 # End of conversation
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try:
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user_input = "/stop"
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bot_response = "conversation-terminated"
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print(f"Bot : ",bot_response)
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self.db.insert_chat_data(self.chat_id_pk, user_input, bot_response)
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self.db.insert_chat_history(current_time, is_stream)
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except Exception as e:
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raise ValueError(f"Failed to update chat history: {e}")
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def continue_chat(self) -> None:
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"""
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Retain only the last few entries if the conversation exceeds 1000 messages.
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"""
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if len(self.conversation_history) > 1000:
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self.conversation_history = self.conversation_history[-3:]
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199
neural_network/chatbot/db.py
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199
neural_network/chatbot/db.py
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import os
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from dotenv import load_dotenv
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import mysql.connector
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from mysql.connector import MySQLConnection
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load_dotenv()
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class Database:
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"""
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A class to manage the connection to the MySQL database using configuration from environment variables.
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Attributes:
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-----------
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config : dict
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The database connection parameters like user, password, host, and database name.
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"""
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def __init__(self) -> None:
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self.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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def connect(self) -> MySQLConnection:
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"""
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Establish a connection to the MySQL database.
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Returns:
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--------
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MySQLConnection
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A connection object for interacting with the MySQL database.
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Raises:
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-------
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mysql.connector.Error
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If the connection to the database fails.
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"""
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return mysql.connector.connect(**self.config)
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class ChatDatabase:
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"""
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A class to manage chat-related database operations, such as creating tables,
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inserting chat history, and retrieving chat data.
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Attributes:
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-----------
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db : Database
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An instance of the `Database` class for establishing connections to the MySQL database.
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"""
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def __init__(self, db: Database) -> None:
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self.db = db
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def create_tables(self) -> None:
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"""
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Create the necessary tables for chat history and chat data in the database.
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If the tables already exist, they will not be created again.
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Raises:
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-------
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mysql.connector.Error
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If there is any error executing the SQL statements.
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"""
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conn = self.db.connect()
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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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cursor.close()
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conn.close()
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def insert_chat_history(self, start_time: str, is_stream: int) -> None:
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"""
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Insert a new chat history record into the database.
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Parameters:
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-----------
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start_time : str
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The starting time of the chat session.
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is_stream : int
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An integer indicating whether the chat is in progress (1) or ended (2).
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Raises:
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-------
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mysql.connector.Error
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If there is any error executing the SQL statements.
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"""
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conn = self.db.connect()
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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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cursor.close()
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conn.close()
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def get_latest_chat_id(self) -> int:
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"""
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Retrieve the chat ID of the most recent chat session from the database.
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Returns:
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--------
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int
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The ID of the latest chat session.
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Raises:
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-------
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mysql.connector.Error
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If there is any error executing the SQL statements.
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"""
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conn = self.db.connect()
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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 WHERE
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chat_id=(SELECT MAX(chat_id) FROM ChatDB.Chat_history)
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"""
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)
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chat_id_pk = cursor.fetchone()[0]
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cursor.close()
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conn.close()
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return chat_id_pk
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def insert_chat_data(
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self, chat_id: int, user_message: str, assistant_message: str
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) -> None:
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"""
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Insert a new chat data record into the database.
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Parameters:
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-----------
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chat_id : int
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The ID of the chat session to which this data belongs.
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user_message : str
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The message provided by the user in the chat session.
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assistant_message : str
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The response from the assistant in the chat session.
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Raises:
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-------
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mysql.connector.Error
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If there is any error executing the SQL statements.
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"""
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conn = self.db.connect()
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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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cursor.close()
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conn.close()
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78
neural_network/chatbot/llm_service.py
Normal file
78
neural_network/chatbot/llm_service.py
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import os
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from together import Together
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from groq import Groq
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from dotenv import load_dotenv
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from typing import List, Dict
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load_dotenv()
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class LLMService:
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"""
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A class to interact with different LLM (Large Language Model) API services, such as Together and Groq.
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Attributes:
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-----------
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api_service : str
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The name of the API service to use ("Together" or "Groq").
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"""
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def __init__(self, api_service: str) -> None:
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"""
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Initialize the LLMService with a specific API service.
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Parameters:
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-----------
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api_service : str
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The name of the LLM API service, either "Together" or "Groq".
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"""
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self.api_service = api_service
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def generate_response(self, conversation_history: List[Dict[str, str]]) -> str:
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"""
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Generate a response from the specified LLM API based on the conversation history.
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Parameters:
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-----------
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conversation_history : List[Dict[str, str]]
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The list of conversation messages, where each message is a dictionary with 'role' and 'content' keys.
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Returns:
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--------
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str
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The generated response content from the assistant.
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Raises:
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-------
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ValueError
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If the specified API service is neither "Together" nor "Groq".
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"""
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if self.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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elif self.api_service == "Groq":
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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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else:
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raise ValueError(f"Unsupported API service: {self.api_service}")
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# Extracting the content of the generated response
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return response.choices[0].message.content
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44
neural_network/chatbot/main.py
Normal file
44
neural_network/chatbot/main.py
Normal file
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from db import Database, ChatDatabase
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from llm_service import LLMService
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from chatbot import Chatbot
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from typing import NoReturn
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def main() -> NoReturn:
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"""
|
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Main function to initialize and start the chatbot application.
|
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|
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This function initializes the database and LLM service, creates necessary tables, and starts
|
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the chatbot for user interaction.
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"""
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# Initialize and configure the database
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db = Database()
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chat_db = ChatDatabase(db)
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chat_db.create_tables()
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|
||||
# Set the API service to either "Together" or "Groq"
|
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api_service = (
|
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"Groq" # Can be set dynamically based on user preference or environment
|
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)
|
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llm_service = LLMService(api_service)
|
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|
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# Initialize the Chatbot with the database and LLM service
|
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chatbot = Chatbot(chat_db, llm_service)
|
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|
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print("Welcome to the chatbot! Type '/stop' to end the conversation.")
|
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chatbot.start_chat()
|
||||
|
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# Chat loop to handle user input
|
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while True:
|
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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(
|
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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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57
neural_network/chatbot/requirements.txt
Normal file
57
neural_network/chatbot/requirements.txt
Normal file
@ -0,0 +1,57 @@
|
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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
|
||||
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Reference in New Issue
Block a user