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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**.

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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()

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neural_network/chatbot/requirements.txt
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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