README.md

Generative AI

We will cover series of generative ai tasks which include but not limited to:

• Retrieval-Augmented Generation (RAG)
• LLM Page Summarization
• Retrieval-based Chatbots
• so on...

Tasks

Retrieval Augmented Generation with LLM Part 1

RAG is a process of fetching up to date or context specific data from an external database and making it available to to an LLM when asking it to generate a response.

To be able to do this, we need an open source language model, a vector database and a composer. Fortunately, there are freely available open source python libraries to create this solution. For simplicity, we will use the following:

• Pre-trained T5 model from Huggingface as LLM
• ChromaDB as vector database
• Langchain as application tools.

Click here for more.

Retrieval Augmented Generation with LLM Part 2

Instead of using a pre-trained T5 model, we will use gpt4all models.

Click here for more.

LLM Page Summarization

To summarize a page, we will use a GPT4All as LLM.

For more, click here

Creating a simple chatbot with open-source LLMs using Python and Hugging Face

Simple but functional Chatbot.

Click here for more.

Hi, I'm Ade! 👋

🚀 About Me

I'm a full stack AI developer...

Authors

• @aakinlalu

🔗 Links

Acknowledgements

• langchain
• GPT4ALL