Day 11 – Develop ChatBot from Scratch with SQL Database including Memory
Today, we will be focusing on developing a ChatBot from scratch with the use of SQL database to store and retrieve information, as well as incorporating memory to enhance the user experience.
Why use SQL database?
SQL database provides a structured way to store and retrieve data, making it ideal for ChatBots that require access to a large amount of information. By using SQL database, we can easily manage and query data to provide relevant responses to the user’s queries.
How to integrate memory into ChatBot?
Integrating memory into ChatBot involves storing information about the user’s interactions and preferences to personalize the conversation. This can include remembering previous queries, user preferences, and any other relevant information that can enhance the user experience. By using memory, the ChatBot can provide more personalized and contextually relevant responses to the user’s queries.
Getting started with developing a ChatBot
1. Set up a SQL database to store and retrieve information.
2. Define the conversation flow and logic for the ChatBot.
3. Integrate memory to store user information and preferences.
4. Test the ChatBot and refine the conversation flow based on user feedback.
Conclusion
Developing a ChatBot from scratch with SQL database including memory can greatly enhance the user experience by providing personalized and relevant responses. By following the steps outlined above, you can create a ChatBot that is not only intelligent and responsive, but also able to remember and learn from past interactions with users.
How I can make this production ready? Also what if I host it (streamlit) do i have to give my db details on cloud?
Does this work with Gpt 3.5 turbo ?
Also can you please make a video similar but instead of using sqlite, we use common dbs like MySql for example? All the YT videos available and examples available use SQL lite just because its a lot easier to use but your videos has always been doing different things and I’d highly appreciate doing something different like a different type of DB
I have a question about the parser. So you are using llm | parser so does that mean the output parser does not take what our prompt is into consideration as well since its just llm and parser?