The author then explains how to integrate the chain into the Gradio UI, allowing users to interact with the model. A function called stream_response is created to handle both the input text and conversation history, streaming responses from the AI. The article concludes with a demonstration of a user interacting with the database through a Gradio Chat Interface. The author promises to cover more details in future articles.
Key takeaways:
- The article explores the ability to interact with Large Language Models (LLMs) that can use your own database to extract data, analyse it, and display it back to you.
- Two key tools used in this process are LangChain, a framework for building applications using LLMs, and Gradio, a Python library for creating web interfaces for machine learning models.
- The author demonstrates how to set up a model to generate SQL queries from a user question, execute the query on the database, and use the results to generate a natural language answer to the user question.
- The final step involves plugging the chain into Gradio to create a user interface that allows users to interact with the model, which translates user input into SQL queries, retrieves the information, and returns the details to the user.