diff --git a/neural_network/chatbot/README.md b/neural_network/chatbot/README.md
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+
+
+# Chatbot with LLM Integration and Database Storage
+
+This chatbot application integrates LLM (Large Language Model) API services, **Together** and **Groq**(you can use any one of them), to generate AI-driven responses. It stores conversation history in a MySQL database and manages chat sessions with triggers that update the status of conversations automatically.
+
+## Features
+- Supports LLM response generation using **Together** and **Groq** APIs.
+- Stores chat sessions and message exchanges in MySQL database tables.
+- Automatically updates chat session status using database triggers.
+- Manages conversation history with user-assistant interaction.
+
+## Requirements
+
+Before running the application, ensure the following dependencies are installed:
+
+- Python 3.13+
+- MySQL Server
+- The following Python libraries:
+  ```bash
+  pip3 install -r requirements.txt
+  ```
+
+## Setup Instructions
+
+### Step 1: Set Up Environment Variables
+
+Create a `.env` file in the root directory of your project and add the following entries for your database credentials and API keys:
+
+```
+# Together API key
+TOGETHER_API_KEY="YOUR_API_KEY"
+
+# Groq API key
+GROQ_API_KEY = "YOUR_API_KEY"
+
+# MySQL connectionDB (if you're running locally)
+DB_USER = "<DB_USER_NAME>"
+DB_PASSWORD = "<DB_USER_NAME>"
+DB_HOST = "127.0.0.1"
+DB_NAME = "ChatDB"
+PORT = "3306"
+
+# API service to you(or use "Together")
+API_SERVICE = "Groq"
+```
+
+### Step 2: Create MySQL Tables and Trigger
+
+The `create_tables()` function in the script automatically creates the necessary tables and a trigger for updating chat session statuses. To ensure the database is set up correctly, the function is called at the beginning of the script.
+
+Ensure that your MySQL server is running and accessible before running the code.
+
+### Step 3: Run the Application
+
+To start the chatbot:
+
+1. Ensure your MySQL server is running.
+2. Open a terminal and run the Python script:
+
+```bash
+python3 chat_db.py
+```
+
+The chatbot will initialize, and you can interact with it by typing your inputs. Type `/stop` to end the conversation.
+
+### Step 4: Test and Validate Code
+
+This project uses doctests to ensure that the functions work as expected. To run the doctests:
+
+```bash
+python3 -m doctest -v chatbot.py
+```
+
+Make sure to add doctests to all your functions where applicable, to validate both valid and erroneous inputs.
+
+### Key Functions
+
+- **create_tables()**: Sets up the MySQL tables (`Chat_history` and `Chat_data`) and the `update_is_stream` trigger.
+- **insert_chat_history()**: Inserts a new chat session into the `Chat_history` table.
+- **insert_chat_data()**: Inserts user-assistant message pairs into the `Chat_data` table.
+- **generate_llm_response()**: Generates a response from the selected LLM API service, either **Together** or **Groq**.
+
diff --git a/neural_network/chatbot/chat_db.py b/neural_network/chatbot/chat_db.py
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+"""
+credits : https://medium.com/google-developer-experts/beyond-live-sessions-building-persistent-memory-chatbots-with-langchain-gemini-pro-and-firebase-19d6f84e21d3
+"""
+
+import os
+import datetime
+import mysql.connector
+from dotenv import load_dotenv
+from together import Together
+from groq import Groq
+import unittest
+from unittest.mock import patch
+from io import StringIO
+
+load_dotenv()
+
+# Database configuration
+db_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"),
+}
+
+
+class LLMService:
+    def __init__(self, api_service: str):
+        self.api_service = api_service
+        if self.api_service == "Together":
+            self.client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
+        else:
+            self.client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+    def generate_response(self, conversation_history: list[dict]) -> str:
+        """
+        Generate a response from the LLM based on the conversation history.
+
+        Example:
+        >>> llm_service = LLMService(api_service="Groq")
+        >>> response = llm_service.generate_response([{"role": "user", "content": "Hello"}])
+        >>> isinstance(response, str)
+        True
+        """
+        if self.api_service == "Together":
+            response = self.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,
+            )
+        else:
+            response = self.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,
+            )
+
+        return response.choices[0].message.content
+
+
+class ChatDB:
+    @staticmethod
+    def create_tables() -> None:
+        """
+        Create the ChatDB.Chat_history and ChatDB.Chat_data tables
+        if they do not exist. Also, create a trigger to update is_stream
+        in Chat_data when Chat_history.is_stream is updated.
+
+        Example:
+        >>> ChatDB.create_tables()
+        Tables and trigger created successfully
+        """
+        try:
+            conn = mysql.connector.connect(**db_config)
+            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()
+            print("Tables and trigger created successfully")
+        except mysql.connector.Error as err:
+            print(f"Error: {err}")
+        finally:
+            cursor.close()
+            conn.close()
+
+    @staticmethod
+    def insert_chat_history(start_time: datetime.datetime, is_stream: int) -> int:
+        """
+        Insert a new row into the ChatDB.Chat_history table and return the inserted chat_id.
+
+        Example:
+        >>> from datetime import datetime
+        >>> chat_id = ChatDB.insert_chat_history(datetime(2024, 1, 1, 12, 0, 0), 1)
+        >>> isinstance(chat_id, int)
+        True
+        """
+        try:
+            conn = mysql.connector.connect(**db_config)
+            cursor = conn.cursor()
+            cursor.execute(
+                """
+                INSERT INTO ChatDB.Chat_history (start_time, is_stream)
+                VALUES (%s, %s)
+                """,
+                (start_time, is_stream),
+            )
+            conn.commit()
+            cursor.execute("SELECT LAST_INSERT_ID()")
+            chat_id = cursor.fetchone()[0]
+            print("Chat history inserted successfully.")
+            return chat_id
+        except mysql.connector.Error as err:
+            print(f"Error: {err}")
+            return None
+        finally:
+            cursor.close()
+            conn.close()
+
+    @staticmethod
+    def get_latest_chat_id() -> int:
+        """
+        Retrieve the latest chat_id from the ChatDB.Chat_history table.
+        :return: The latest chat_id or None if no chat_id exists.
+
+        Example:
+        >>> chat_id = ChatDB.get_latest_chat_id()
+        >>> isinstance(chat_id, int)
+        True
+        """
+        try:
+            conn = mysql.connector.connect(**db_config)
+            cursor = conn.cursor()
+            cursor.execute(
+                """
+                SELECT chat_id FROM ChatDB.Chat_history
+                ORDER BY chat_id DESC LIMIT 1
+                """
+            )
+            chat_id = cursor.fetchone()[0]
+            return chat_id if chat_id else None
+        except mysql.connector.Error as err:
+            print(f"Error: {err}")
+            return None
+        finally:
+            cursor.close()
+            conn.close()
+
+    @staticmethod
+    def insert_chat_data(
+        chat_id: int, user_message: str, assistant_message: str
+    ) -> None:
+        """
+        Insert a new row into the ChatDB.Chat_data table.
+
+        Example:
+        >>> ChatDB.insert_chat_data(1, 'Hello', 'Hi there!')
+        Chat data inserted successfully.
+        """
+        try:
+            conn = mysql.connector.connect(**db_config)
+            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()
+            print("Chat data inserted successfully.")
+        except mysql.connector.Error as err:
+            print(f"Error: {err}")
+        finally:
+            cursor.close()
+            conn.close()
+
+
+class Chatbot:
+    def __init__(self, api_service: str):
+        self.llm_service = LLMService(api_service)
+        self.conversation_history = []
+        self.chat_id_pk = None
+        self.start_time = datetime.datetime.now(datetime.timezone.utc)
+
+    def chat_session(self) -> None:
+        """
+        Start a chatbot session, allowing the user to interact with the LLM.
+        Saves conversation history in the database and ends the session on "/stop" command.
+
+        Example:
+        >>> chatbot = Chatbot(api_service="Groq")
+        >>> chatbot.chat_session()  # This will be mocked in the tests
+        Welcome to the chatbot! Type '/stop' to end the conversation.
+        """
+        print("Welcome to the chatbot! Type '/stop' to end the conversation.")
+
+        while True:
+            user_input = input("\nYou: ").strip()
+            self.conversation_history.append({"role": "user", "content": user_input})
+
+            if self.chat_id_pk is None:
+                if user_input.lower() == "/stop":
+                    break
+                bot_response = self.llm_service.generate_response(
+                    self.conversation_history
+                )
+                self.conversation_history.append(
+                    {"role": "assistant", "content": bot_response}
+                )
+
+                is_stream = 1  # New conversation
+                self.chat_id_pk = ChatDB.insert_chat_history(
+                    self.start_time, is_stream
+                )  # Return the chat_id
+                if self.chat_id_pk:
+                    ChatDB.insert_chat_data(self.chat_id_pk, user_input, bot_response)
+            else:
+                if user_input.lower() == "/stop":
+                    is_stream = 2  # End of conversation
+                    current_time = datetime.datetime.now(datetime.timezone.utc)
+                    ChatDB.insert_chat_history(current_time, is_stream)
+                    break
+
+                bot_response = self.llm_service.generate_response(
+                    self.conversation_history
+                )
+                self.conversation_history.append(
+                    {"role": "assistant", "content": bot_response}
+                )
+
+                is_stream = 0  # Continuation of conversation
+                current_time = datetime.datetime.now(datetime.timezone.utc)
+                ChatDB.insert_chat_history(current_time, is_stream)
+                ChatDB.insert_chat_data(self.chat_id_pk, user_input, bot_response)
+
+            if len(self.conversation_history) > 1000:
+                self.conversation_history = self.conversation_history[-3:]
+
+
+# Test cases for Chatbot
+class TestChatbot(unittest.TestCase):
+    @patch("builtins.input", side_effect=["Hello", "/stop"])
+    @patch("sys.stdout", new_callable=StringIO)
+    def test_chat_session(self, mock_stdout, mock_input):
+        """
+        Test the chat_session method for expected welcome message.
+        """
+        chatbot = Chatbot(api_service="Groq")
+        chatbot.chat_session()
+
+        # Check for the welcome message in the output
+        output = mock_stdout.getvalue().strip().splitlines()
+        self.assertIn(
+            "Welcome to the chatbot! Type '/stop' to end the conversation.", output
+        )
+        self.assertTrue(
+            any("Chat history inserted successfully." in line for line in output)
+        )
+        self.assertTrue(
+            any("Chat data inserted successfully." in line for line in output)
+        )
+
+
+if __name__ == "__main__":
+    #
+    ChatDB.create_tables()
+    unittest.main()
diff --git a/neural_network/chatbot/requirements.txt b/neural_network/chatbot/requirements.txt
new file mode 100644
index 000000000000..0f1204243a5d
--- /dev/null
+++ b/neural_network/chatbot/requirements.txt
@@ -0,0 +1,57 @@
+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