Overview of the Course

In this comprehensive course, we’ll dive into an exciting journey of creating a cutting-edge customer support chatbot tailored specifically for car owners. We’ll leverage the latest advancements in large language models, harness the efficiency of LangChain for streamlined data processing, and create a user-friendly interface using Streamlit.

Our goal is to equip you with the skills and knowledge to build a chatbot that empowers car owners to access information about their vehicles quickly and effortlessly. You’ll dive deep into the world of large language models, discovering the power of fine-tuning with augmented data.

• A good understanding of Python programming is required, including experience with basic syntax and data structures.

• thatFamiliarity with natural language processing concepts and techniques which handle text manipulation.

• Familiarity with utilizing novel development tools and environments, such as Visual Studio Code, PyCharm, Jupyter Notebook, Streamlit, and LangChain, is essential.

Throughout the course, we’ll use the following libraries, tools, and platforms:

• Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its intuitive data structures make it very attractive for rapid application development, as well as for use as a scripting or glue language to connect existing components together.

• Pandas is a powerful and versatile Python library for data manipulation and analysis. It provides a wide range of functions and features that make it easier to work with data sets, clean messy data, and derive insights from data.

• LangChain is an open-source framework for developing applications powered by large language models (LLMs). It provides tools and abstractions to improve the customization, accuracy, and relevancy of the information generated by LLMs.

• Streamlit is an open-source Python library that allows developers to build and share data applications quickly and easily. It simplifies the process of turning data scripts into interactive web apps, enabling developers to create and deploy applications in minutes rather than weeks. Streamlit allows developers to develop world-class prototype applications or minimum viable products (MVP) with a simple learning curve.

• Hugging Face is an AI community and company that focuses on the field of artificial intelligence, specifically in natural language processing (NLP) and computer vision. It provides a platform for collaboration, learning, and sharing work related to NLP and computer vision models. Hugging Face allows us to utilize or download open-source models that are saved as pretrained transformer models from their library.

The above libraries, tools, and frameworks will help us in developing a production-grade chatbot in the following manner:

• Python: This serves as the foundational programming language for developing the chatbot’s core functionalities.

• Pandas: This is utilized for data manipulation and processing the chatbot training data and user inputs.

• LangChain: This enables efficient integration of large language models into the chatbot, enhancing its ability to understand and generate human-like responses.

• Streamlit: This facilitates the rapid development of interactive web interfaces as a prototype, allowing for the easy deployment of the chatbot as a web application.

• Hugging Face: This provides access to a repository of pretrained models and datasets, which are essential in building an AI-powered chatbot.

• This course is created for data scientists, data engineers, data analysts, machine learning engineers, and Python developers who want to learn or develop a new expertise in the realm of artificial intelligence.

• Python developers will gain immense experience in manipulating data in various formats and extracting and transforming text while coding with Python and pandas libraries.

• Data practitioners will find this course an essential tool for developing large language models and, in particular LLM, LLM-powered chatbots.

• AI managers and data managers will gain valuable insight into the world of large language models.

Throughout the course, we’ll gain hands-on experience building, fine-tuning, and deploying the chatbot, ensuring it meets the highest standards of functionality and user satisfaction. By the end of this journey, we’ll have a powerful customer support tool at our disposal, ready to assist car owners in their journey to a seamless and informed car ownership experience.

By the end of this course, learners will be capable of:

• Utilizing and integrating open-source large language model using the Hugging Face platform.

• Uploading and reading various formats of documents, transforming these documents and preparing them in order to be read by large language models, and uploading and pulling them back from vector data stores.

• Developing a chain of actions that the large language model will utilize to give a response to user questions.

• Quickly prototyping a user interface that will host the chatbot, allowing the user to seamlessly interact with it.

• Deploying the chatbot to Streamlit community cloud so that users can start using the chatbot.