C01:- Chatbot

                      Workflow of Chatbot

  • MySQL: For storing and managing user data.
  • Python: For implementing the core logic of the chatbot.
  • Large Language Model (LLM): For generating human-like responses.
  • Flask: For creating a web server that handles incoming requests.

    • Detailed Workflow:

    Step 1: User Query Handling

    1. User Question: The user asks a question through the chatbot interface.
    2. Flask Server: The Flask server receives the user's question.

    Step 2: Processing Query

    1. Query Processing: The Flask server processes the user’s question. Depending on the question, it might:
      • Directly fetch information from a pre-defined database (if needed).
      • Generate a prompt for the Large Language Model (e.g., Gemini).

    Step 3: Generating Responses

    1. Prompt Creation: The Flask server prepares a prompt that includes the user's question.
    2. LLM Invocation: The prompt is sent to the Large Language Model.
    3. Response Generation: The LLM processes the prompt and generates a response.

    Step 4: Sending Response

    1. Response Delivery: The Flask server receives the response from the LLM and sends it back to the user.


    Flow Diagram




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