Building an AI Data Assistant with Streamlit, LangChain and OpenAI | Part 2

It is no secret that Large Language Models are revolutionising the world of AI. Start-ups across the board are swiftly integrating these models into their frameworks in order to streamline their processes and generate sales. And guess what? So can you! In this video of our series Building an AI Data Assistant with Streamlit, LangChain and OpenAI, we’ll continue our journey to simplify your data science tasks. We’ll pick up from where we left off and I’ll guide you through the process of enhancing your AI Assistant. We will dive into the concept of prompts, prompt templates, chains and tools in the Lanchain framework. This video is part of the series Building an AI Assistant to make your data science life easier in which we will develop an AI assistant using Streamlit, LangChain and OpenAI’s GPT models, designed to help users with their data science projects. This AI assistant will streamline the entire process of a data science project, including exploratory data analysis (EDA), model selection and prediction, saving valuable time and resources. I'll walk you through the entire process, from installing the required libraries to solving a machine learning problem using AI. By the end of this series, you will have a powerful tool at your disposal, ready to assist you in every step of your data science journey. If you missed part 1, don’t worry! You can catch up here 👉 https://www.seevid.ir/fa/w/CBRE_Me1IQ0 If you want to take a deeper dive in data science, check out our library of courses on digiLab Academy 🔗 https://academy.digilab.co.uk/courses ———————————————————————— 🎵 Music ———————————————————————— Downtown Walk by | e s c p | https://escp-music.bandcamp.com/ Music promoted by https://www.free-stock-music.com/ Creative Commons / Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ ———————————————————————— 📌 Timestamps ———————————————————————— Intro - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Agenda - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Required libraries and modules - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Starting a new section - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Our first prompt - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Using a prompt template - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Adding our first chain - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Introducing our second prompt template - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Adding another chain - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Linking our chains together with a simple sequential chain - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Outputting multiple outputs: using a sequential chain - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Adding our first tool: Wikipedia API wrapper - https://www.seevid.ir/fa/w/4ChcBu0eY2Q @st.cache_data vs @st.cache_resource - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Creating a selection box - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Creating a Python agent - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Using Our Python Agent to give a solution to our problem - https://www.seevid.ir/fa/w/4ChcBu0eY2Q Further enhancing user experience - adding text area and two more expanders - https://www.seevid.ir/fa/w/4ChcBu0eY2Q What’s next - https://www.seevid.ir/fa/w/4ChcBu0eY2Q ———————————————————————— 📌 Resources ———————————————————————— Written tutorial - https://academy.digilab.co.uk/posts/expanding-our-ai-data-assistant-to-use-prompt-templates-and-chains Streamlit documentation - https://streamlit.io/ Open AI website - https://openai.com/ LangChain documentation - https://python.langchain.com/ ———————————————————————— 👱🏻‍♀️ Connect with me ———————————————————————— digiLab Academy - https://academy.digilab.co.uk/ Twitter - @digiLab_Academy LinkedIn: https://www.linkedin.com/company/digilab-solutions-ltd ———————————————————————— 🏷️ Tags ———————————————————————— Building an AI assistant to make your data science life easier Simplifying your data science journey with an AI assistant Crafting an AI assistant using Streamlit, Langchain and OpenAI models Enhancing data science efficiency through an AI-driven assistant Creating an AI assistant to ease your path in data science Developing a data science ally using Streamlit, Langchain and OpenAI models Developing an AI assistant for smoother workflows Designing an AI assistant to simplify your data science projects Making data science easy with the aid of an intelligent assistant Making data science effortless with the implementation of an AI assistant ———————————————————————— ✨ Hashtags ———————————————————————— [#howtobuildanaiassistant] () [#AIassistant] () [#BuildyourownAIassistant] () [#Python] () [#Streamlit] () [#LangChain] () [#LargeLanguageModels] () [#Datascience] () [#Machinelearning] () [#AI] () [#Artificialintelligence] () [#Tutorial] () [#Datascienceproject] () [#OpenAI] () [#GPT] ()

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