From 508249e47ac72ce3ffad67832bb0860fbabc7b58 Mon Sep 17 00:00:00 2001
From: Pritam Das <69068731+Pritam3355@users.noreply.github.com>
Date: Sun, 20 Oct 2024 12:00:55 +0530
Subject: [PATCH] Update README.md

---
 neural_network/chatbot/README.md | 116 +++++++++++++++++++------------
 1 file changed, 72 insertions(+), 44 deletions(-)

diff --git a/neural_network/chatbot/README.md b/neural_network/chatbot/README.md
index 5e8f86b0c..acddd4c8f 100644
--- a/neural_network/chatbot/README.md
+++ b/neural_network/chatbot/README.md
@@ -1,55 +1,83 @@
-# Chatbot with Chat history stored in Database
 
-This project is a simple chatbot application built using Python, integrating a database for chat history storage and a language model service to generate responses. The chatbot can handle user messages, manage chat history, and terminate conversations upon receiving a `/stop` command.
+
+# 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
-- **Conversation Handling**: The bot processes user inputs and generates responses using a language model service.
-- **Database Integration**: Stores chat data (user messages and bot responses) and maintains chat history.
-- **Session Management**: Supports starting and terminating chat sessions, including proper logging of start and end times.
-- **Message Truncation**: Limits conversation history to the last few messages if the conversation exceeds a large number of entries.
+- 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.
 
-## Components
-- **`Chatbot` Class**: Core logic for handling user messages and managing the chat lifecycle.
-- **`Database` (Mocked in tests)**: Handles chat data storage (methods for inserting and retrieving data).
-- **`LLM Service` (Mocked in tests)**: Generates responses to user input based on conversation history.
+## Requirements
 
-## Installation
-1. Clone the repository:
-2. Install the necessary dependencies
-   ```bash
-   pip3 install requirements.txt
-   ```
-4. Run the bot or test it using `doctest`:
-    ```bash
-    python3 -m doctest -v chatbot.py
-    ```
+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:
 
-## Usage
-1. **Create Database**: Create a databse named `ChatDB` in Mysql
-2. **Create .env**:
 ```
-  # Together API key
-  TOGETHER_API_KEY="YOUR_API_KEY"
+# Together API key
+TOGETHER_API_KEY="YOUR_API_KEY"
 
-  # Groq API key
-  GROQ_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"
- ```
-7. **Handling Messages**: run below command to start the chat in console, you can login to your Database to check the chat history
-```python
-python3 main.py
+# 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"
 ```
-10. **Ending the Chat**: When the user sends `/stop`, the chat will terminate and log the end of the conversation with the message 'conversation-terminated'
 
-## Testing
-The code includes basic `doctests` to verify the chatbot's functionality using mock services for the database and language model:
-- Run the tests:
-    ```bash
-    python3 -m doctest -v chatbot.py
-    ```
+### 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**.
+