Building a Language Model App: A Cost-Effective Solution for Your Business

:computer:Building a language model app using retrieval augmented generation is a cost-effective solution for businesses.

:speech_balloon:Streamlit is a powerful tool for creating interactive interfaces in the language model app.

:floppy_disk:The app can handle user prompts, display chat history, and show responses from the language model.

:gear:Custom data can be incorporated into the app, allowing for personalized interactions with the language model.

:question:The Q&A capabilities of the language model app enable users to ask and get answers to specific questions.

What is retrieval augmented generation?

—Retrieval augmented generation is a technique where chunks of data are used as prompts for a language model to generate responses based on the context.

What is Streamlit?

—Streamlit is a framework used to create interactive interfaces for Python applications, including language model apps.

Can the app handle user prompts and display chat history?

—Yes, the app can handle user prompts and display the chat history, allowing users to have interactive conversations with the language model.

Is it possible to incorporate custom data into the app?

—Yes, the app allows for the incorporation of custom data, enabling personalized interactions with the language model.

Does the app support Q&A capabilities?

—Yes, the language model app supports Q&A capabilities, allowing users to ask specific questions and receive answers from the model.

00:00Introduction to building a language model app using retrieval augmented generation.

00:09Explanation of retrieval augmented generation technique and its benefits for businesses.

00:20Importing dependencies and using Streamlit to create an interactive interface for the app.

00:32Handling user prompts and displaying messages in the app using the chat input and chat message components.

00:46Adding chat history functionality to the app to display all user messages.

01:14Integrating the language model into the app using the Lang chain interface and specifying decoding parameters.

01:38Configuring the language model and using the llama 270b chat model for chat responses.

02:07Incorporating custom data into the app and utilizing the Lang chain vector store index creator.