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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**.
+