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84 lines
2.7 KiB
Markdown
84 lines
2.7 KiB
Markdown
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# Chatbot with LLM Integration and Database Storage
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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.
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## Features
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- Supports LLM response generation using **Together** and **Groq** APIs.
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- Stores chat sessions and message exchanges in MySQL database tables.
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- Automatically updates chat session status using database triggers.
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- Manages conversation history with user-assistant interaction.
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## Requirements
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Before running the application, ensure the following dependencies are installed:
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- Python 3.13+
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- MySQL Server
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- The following Python libraries:
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```bash
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pip3 install -r requirements.txt
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```
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## Setup Instructions
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### Step 1: Set Up Environment Variables
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Create a `.env` file in the root directory of your project and add the following entries for your database credentials and API keys:
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```
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# Together API key
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TOGETHER_API_KEY="YOUR_API_KEY"
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# Groq API key
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GROQ_API_KEY = "YOUR_API_KEY"
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# MySQL connectionDB (if you're running locally)
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DB_USER = "<DB_USER_NAME>"
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DB_PASSWORD = "<DB_USER_NAME>"
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DB_HOST = "127.0.0.1"
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DB_NAME = "ChatDB"
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PORT = "3306"
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# API service to you(or use "Together")
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API_SERVICE = "Groq"
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```
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### Step 2: Create MySQL Tables and Trigger
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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.
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Ensure that your MySQL server is running and accessible before running the code.
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### Step 3: Run the Application
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To start the chatbot:
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1. Ensure your MySQL server is running.
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2. Open a terminal and run the Python script:
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```bash
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python3 chat_db.py
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```
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The chatbot will initialize, and you can interact with it by typing your inputs. Type `/stop` to end the conversation.
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### Step 4: Test and Validate Code
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This project uses doctests to ensure that the functions work as expected. To run the doctests:
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```bash
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python3 -m doctest -v chatbot.py
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```
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Make sure to add doctests to all your functions where applicable, to validate both valid and erroneous inputs.
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### Key Functions
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- **create_tables()**: Sets up the MySQL tables (`Chat_history` and `Chat_data`) and the `update_is_stream` trigger.
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- **insert_chat_history()**: Inserts a new chat session into the `Chat_history` table.
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- **insert_chat_data()**: Inserts user-assistant message pairs into the `Chat_data` table.
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- **generate_llm_response()**: Generates a response from the selected LLM API service, either **Together** or **Groq**.
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